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Data Scientists

15-2051.00 Bright Outlook $158K
16
postings · Doctorate
Career stage: All Intern (18) Entry-level (23) Mid-level (447) Senior (713) Manager (182) Director (151)
Education: All Bachelor's (52) Master's (77) Doctorate (149)
What do these filters mean?
Career stage is read from the job title. Use it to find jobs aimed at where you are right now:
  • Intern You can apply while still in school.
  • Entry-level Designed for new graduates.
  • Mid-level Typically expects internship or 2-3 years of experience.
  • Senior Established career role — usually 5+ years experience.
  • Manager Leads a team of engineers, not an early-career role.
  • Director Executive role — typically 10+ years of career experience.
Education is the highest degree the posting explicitly mentions. Postings that don't say are not filtered out — they appear under "All".
Machine Learning Engineering Manager
Indeed · Seattle, WA
Manager Doctorate
2026-06-03
Responsibilities
  • At Indeed, we are committed to delivering exceptional experiences that connect job seekers with opportunities through innovative technology. We integrate machine learning at every step to create consistent, engaging, and secure experiences that meet the needs of our users. Our teams consist of Software Engineers, UX Designers, Product Managers, and Machine Learning professionals collaborating across regions to drive impactful business outcomes.
  • As a Machine Learning Engineering Manager, you will onboard and oversee junior scientists and technical leads, partnering closely with Product, Software Engineering, and UX teams. Your focus will include developing and innovating machine learning ecosystems that upgrade job seeker journey experience end to end
  • Coach Machine Learning Engineers and Data Scientists on the Journey team to improve their performance, advise them on their career direction, and develop their qualifications.
  • Work to understand, prioritize, and plan the team's work items without external guidance.
  • Ensure delivery of machine learning solutions, set expectations for what can be done and by when, and prioritize incoming projects.
  • Improve existing Agile, ML, and A/B testing processes and develop new ones.
  • Scope projects, gather and improve on requirements, and delegate work effectively.
  • Partner with and provide project direction and feedback to cross-functional peers, including Product Managers, Software Engineers.
  • Remove roadblocks and give individual contributors autonomy and ownership.
  • Brainstorm with teammates about practical experimental design, navigating production codebases, and model development.
  • Be prepared to closely engage and contribute directly to implementation when necessary.
  • *Skills/Competencies
  • Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master's degree with a minimum of 6 years of experience; or a PhD with a minimum of 3 years experience
  • Demonstrated achievement as a Manager in Machine Learning Engineering, overseeing teams of 3 or more, and addressing intricate, large-scale problems
  • Well-versed in coding (Python, Java, Go, or C++) and experience with SQL Databases like Presto, and data processing frameworks like Spark
  • Have full-stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring
  • Highly effective in coaching Machine Learning Engineers, facilitating qualification enhancement, and fostering career development
View full posting on CareerOneStop → ID: 85c5b878e3fc
Machine Learning Engineering Manager
Indeed · Portland, OR
Manager Doctorate
2026-06-03
Responsibilities
  • At Indeed, we are committed to delivering exceptional experiences that connect job seekers with opportunities through innovative technology. We integrate machine learning at every step to create consistent, engaging, and secure experiences that meet the needs of our users. Our teams consist of Software Engineers, UX Designers, Product Managers, and Machine Learning professionals collaborating across regions to drive impactful business outcomes.
  • As a Machine Learning Engineering Manager, you will onboard and oversee junior scientists and technical leads, partnering closely with Product, Software Engineering, and UX teams. Your focus will include developing and innovating machine learning ecosystems that upgrade job seeker journey experience end to end
  • Coach Machine Learning Engineers and Data Scientists on the Journey team to improve their performance, advise them on their career direction, and develop their qualifications.
  • Work to understand, prioritize, and plan the team's work items without external guidance.
  • Ensure delivery of machine learning solutions, set expectations for what can be done and by when, and prioritize incoming projects.
  • Improve existing Agile, ML, and A/B testing processes and develop new ones.
  • Scope projects, gather and improve on requirements, and delegate work effectively.
  • Partner with and provide project direction and feedback to cross-functional peers, including Product Managers, Software Engineers.
  • Remove roadblocks and give individual contributors autonomy and ownership.
  • Brainstorm with teammates about practical experimental design, navigating production codebases, and model development.
  • Be prepared to closely engage and contribute directly to implementation when necessary.
  • *Skills/Competencies
  • Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master's degree with a minimum of 6 years of experience; or a PhD with a minimum of 3 years experience
  • Demonstrated achievement as a Manager in Machine Learning Engineering, overseeing teams of 3 or more, and addressing intricate, large-scale problems
  • Well-versed in coding (Python, Java, Go, or C++) and experience with SQL Databases like Presto, and data processing frameworks like Spark
  • Have full-stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring
  • Highly effective in coaching Machine Learning Engineers, facilitating qualification enhancement, and fostering career development
View full posting on CareerOneStop → ID: 1dab31464fa0
Machine Learning Engineering Manager
Indeed · Boise, ID
Manager Doctorate
2026-06-03
Responsibilities
  • At Indeed, we are committed to delivering exceptional experiences that connect job seekers with opportunities through innovative technology. We integrate machine learning at every step to create consistent, engaging, and secure experiences that meet the needs of our users. Our teams consist of Software Engineers, UX Designers, Product Managers, and Machine Learning professionals collaborating across regions to drive impactful business outcomes.
  • As a Machine Learning Engineering Manager, you will onboard and oversee junior scientists and technical leads, partnering closely with Product, Software Engineering, and UX teams. Your focus will include developing and innovating machine learning ecosystems that upgrade job seeker journey experience end to end
  • Coach Machine Learning Engineers and Data Scientists on the Journey team to improve their performance, advise them on their career direction, and develop their qualifications.
  • Work to understand, prioritize, and plan the team's work items without external guidance.
  • Ensure delivery of machine learning solutions, set expectations for what can be done and by when, and prioritize incoming projects.
  • Improve existing Agile, ML, and A/B testing processes and develop new ones.
  • Scope projects, gather and improve on requirements, and delegate work effectively.
  • Partner with and provide project direction and feedback to cross-functional peers, including Product Managers, Software Engineers.
  • Remove roadblocks and give individual contributors autonomy and ownership.
  • Brainstorm with teammates about practical experimental design, navigating production codebases, and model development.
  • Be prepared to closely engage and contribute directly to implementation when necessary.
  • *Skills/Competencies
  • Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master's degree with a minimum of 6 years of experience; or a PhD with a minimum of 3 years experience
  • Demonstrated achievement as a Manager in Machine Learning Engineering, overseeing teams of 3 or more, and addressing intricate, large-scale problems
  • Well-versed in coding (Python, Java, Go, or C++) and experience with SQL Databases like Presto, and data processing frameworks like Spark
  • Have full-stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring
  • Highly effective in coaching Machine Learning Engineers, facilitating qualification enhancement, and fostering career development
View full posting on CareerOneStop → ID: fa90c2d42649
Machine Learning Engineering Manager
Indeed · Helena, MT
Manager Doctorate
2026-06-03
Responsibilities
  • At Indeed, we are committed to delivering exceptional experiences that connect job seekers with opportunities through innovative technology. We integrate machine learning at every step to create consistent, engaging, and secure experiences that meet the needs of our users. Our teams consist of Software Engineers, UX Designers, Product Managers, and Machine Learning professionals collaborating across regions to drive impactful business outcomes.
  • As a Machine Learning Engineering Manager, you will onboard and oversee junior scientists and technical leads, partnering closely with Product, Software Engineering, and UX teams. Your focus will include developing and innovating machine learning ecosystems that upgrade job seeker journey experience end to end
  • Coach Machine Learning Engineers and Data Scientists on the Journey team to improve their performance, advise them on their career direction, and develop their qualifications.
  • Work to understand, prioritize, and plan the team's work items without external guidance.
  • Ensure delivery of machine learning solutions, set expectations for what can be done and by when, and prioritize incoming projects.
  • Improve existing Agile, ML, and A/B testing processes and develop new ones.
  • Scope projects, gather and improve on requirements, and delegate work effectively.
  • Partner with and provide project direction and feedback to cross-functional peers, including Product Managers, Software Engineers.
  • Remove roadblocks and give individual contributors autonomy and ownership.
  • Brainstorm with teammates about practical experimental design, navigating production codebases, and model development.
  • Be prepared to closely engage and contribute directly to implementation when necessary.
  • *Skills/Competencies
  • Requires a Bachelor's degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 8 years of related experience; or a Master's degree with a minimum of 6 years of experience; or a PhD with a minimum of 3 years experience
  • Demonstrated achievement as a Manager in Machine Learning Engineering, overseeing teams of 3 or more, and addressing intricate, large-scale problems
  • Well-versed in coding (Python, Java, Go, or C++) and experience with SQL Databases like Presto, and data processing frameworks like Spark
  • Have full-stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring
  • Highly effective in coaching Machine Learning Engineers, facilitating qualification enhancement, and fostering career development
View full posting on CareerOneStop → ID: 86c5ccbc5a93
Data Science Manager, Gen AI - SFL Scientific
Deloitte · Seattle, WA
Manager Doctorate
2026-05-23
Requirements
  • Master's or PhD degree in a relevant STEM field (Data Science, Computer Science, Engineering, Mathematics, Physics, etc.)
  • 6+ years of experience working in data science, data engineering, software engineering, or MLOps
  • 6+ years of experience in AI/ML algorithm development workflow and data analysis in the major data modalities from NLP, time-series analysis, computer vision to graph models
  • 6+ years of experience in core programming languages and data science packages (Python, Keras, PyTorch, Pandas, Scikit-learn, Docker, Kubernetes, etc.)
  • 6+ years of experience with traditional ML and deep learning techniques (CNNs, RNNs, LSTMs, GANs), model tuning, and validation of developed algorithms
  • 4+ years of experience managing teams and delivering complex and critical projects
  • Live within commuting distance to one of Deloitte's consulting offices
  • Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available
Preferred
  • Experience with cloud deployment (AWS, Azure, GCP), such as building and scaling in AWS SageMaker or Azure ML Studio
  • Experience with developing and testing GenAI solutions
  • Experience in a client-facing role or internal AI product development role
  • Highly proficient written and verbal skills to support briefings, proposals, technical sprint plans, solution reports, progress updates, and executive presentations
  • The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $155,600 to $306,800.
  • You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
View full posting on CareerOneStop → ID: b49278c50cf0
Data Science Manager, Gen AI - SFL Scientific
Deloitte · Portland, OR
Manager Doctorate
2026-05-23
Requirements
  • Master's or PhD degree in a relevant STEM field (Data Science, Computer Science, Engineering, Mathematics, Physics, etc.)
  • 6+ years of experience working in data science, data engineering, software engineering, or MLOps
  • 6+ years of experience in AI/ML algorithm development workflow and data analysis in the major data modalities from NLP, time-series analysis, computer vision to graph models
  • 6+ years of experience in core programming languages and data science packages (Python, Keras, PyTorch, Pandas, Scikit-learn, Docker, Kubernetes, etc.)
  • 6+ years of experience with traditional ML and deep learning techniques (CNNs, RNNs, LSTMs, GANs), model tuning, and validation of developed algorithms
  • 4+ years of experience managing teams and delivering complex and critical projects
  • Live within commuting distance to one of Deloitte's consulting offices
  • Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available
Preferred
  • Experience with cloud deployment (AWS, Azure, GCP), such as building and scaling in AWS SageMaker or Azure ML Studio
  • Experience with developing and testing GenAI solutions
  • Experience in a client-facing role or internal AI product development role
  • Highly proficient written and verbal skills to support briefings, proposals, technical sprint plans, solution reports, progress updates, and executive presentations
  • The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $155,600 to $306,800.
  • You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
View full posting on CareerOneStop → ID: 51a9883c9b6b
Data Scientist - Senior Manager- Consulting - Location OPEN
EY · Seattle, WA
Manager Doctorate
2026-05-21
Requirements
  • PhD preferred in Computer Science, Machine Learning, Computational Linguistics, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Master's with exceptional applied experience also considered.
  • 12+ years of applied data science / ML experience, with at least 3 years in a senior technical leadership role.
  • Demonstrable experience taking AI and ML systems from prototype to production - across multiple problem types (e.g., predictive modeling, NLP, retrieval / RAG, optimization, or agentic workflows).
  • Hands-on proficiency in Python and the modern ML/AI stack (PyTorch, Hugging Face, scikit-learn, and at least one of LangChain / LangGraph or an equivalent agent / LLM framework).
  • Practical experience with at least one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI) and one vector / retrieval stack.
  • Experience designing evaluation frameworks for AI systems - beyond standard benchmarks - including evaluation for foundation-model-based or agentic workflows.
  • Experience operating in a client-facing or cross-functional environment, translating business problems into rigorous data-science programs.
  • *Ideally, you will also have
  • Published research or open-source contributions in AI / ML agentic systems, retrieval, memory and grounding, knowledge graphs, foundation models, optimization, or related areas.
  • Experience with knowledge graphs, ontologies, and neuro-symbolic approaches to grounding and reasoning.
  • Experience building or contributing to a cognitive harness, agent operating system, or agent runtime - internal or open source.
  • Experience with continual learning, online evaluation, drift detection, and model / memory monitoring in production.
  • Familiarity with regulated-industry constraints (SOX, HIPAA, GDPR) and how AI design interacts with audit, retention, and explainability.
  • Prior consulting, product, or hyperscaler experience - comfortable in a fast, ambiguous environment with senior stakeholders.
  • *What we look fo
  • We are looking for a data-science leader who is genuinely excited about hard technical problems and who is as comfortable in a research paper as in a production codebase. You should be the kind of person who treats AI systems as engineered products designed, evaluated, and operated with rigor and who wants to lead a team that ships to Fortune 500 clients across multiple industries.
Responsibilities
  • As a Senior Manager, Data Scientist, you will be a hands-on technical leader who shapes the data-science agenda across multiple workstreams. You will own technical direction, experimentation, and the path from prototype to production for the problems you take on. You will be expected to build, not just design and to lift the bar of the team around you.
  • Lead data-science strategy and execution across multiple AI workstreams including agentic AI, the cognitive harness, memory and grounding, foundation-model applications, machine learning, optimization, and applied analytics.
  • Drive the applied-research and experimentation agenda long-context vs. retrieval trade-offs, hybrid search, reranking, evaluation design, model selection, fine-tuning, and emerging techniques as the field evolves.
  • Design and contribute to the build of core platform components including elements of EY's cognitive harness (memory services, retrieval pipelines, grounding layers, evaluation harnesses) and other reusable AI capabilities.
  • Establish evaluation and quality frameworks for AI systems retrieval and grounding fidelity, hallucination rate, model accuracy, latency, cost, fairness, and continuous evaluation in production.
  • Lead and mentor data scientists and ML engineers set the technical bar, run design reviews, and grow rigor in experimentation, model selection, and production readiness.
  • Partner with sector and functional teams (Finance, Risk, Tax, Supply Chain, HR, Operations) to translate business problems into rigorous, deliverable data-science programs.
  • Engage with clients on complex AI problems shape the technical approach, defend it in front of senior stakeholders, and own delivery quality.
  • Stay abreast of AI technology trends and provide directional guidance and recommendations around models, frameworks, tools, and patterns that fit our clients' existing ecosystems.
  • Apply combined business and technical knowledge to develop and execute target architectures that enable implementation, monitoring, and ongoing improvement of AI at scale.
  • *Skills and attributes for success
  • This role will work to deliver tech at speed, innovate at scale, and put humans at the center. You will provide technical guidance and share knowledge with team members with diverse skills and backgrounds. You will consistently deliver quality client services, focusing on more complex, judgmental, and specialized issues surrounding modern AI, foundation models, and emerging technology. You will demonstrate deep technical capabilities and professional knowledge, and you will lead through making.
  • Strong, hands-on data scientist who codes comfortable building and shipping models, pipelines, services, and experimental frameworks, not just specifying them.
  • Deep technical breadth across modern AI foundation models and prompting, retrieval and grounding, embeddings and fine-tuning, classical ML, optimization, and experimentation rigor.
  • Working knowledge of agentic systems and cognitive harness / agent-runtime architectures - memory, tools, policies, evaluation, observability, cost and the trade-offs between them.
  • Familiarity with at least one major agent SDK (Google ADK, AWS Bedrock AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and the ability to compare them critically.
  • Experience designing and operating production AI systems on cloud platforms (GCP, AWS, Azure, Databricks) including responsible-AI guardrails, monitoring, and continuous evaluation.
  • Track record of leading data-science teams across multiple projects setting technical strategy, mentoring, and raising the bar on rigor and delivery quality.
  • Excellent communication skills able to explain complex AI concepts to executive clients, and able to defend technical choices in front of architects, engineers, and researchers.
  • Comfortable moving between problem spaces - equally credible designing a memory architecture, evaluating a forecasting model, or shaping a new agentic workflow.
View full posting on CareerOneStop → ID: d4ce7ce958d6
Data Scientist - Senior Manager- Consulting - Location OPEN
EY · Olympia, WA
Manager Doctorate
2026-05-21
Requirements
  • PhD preferred in Computer Science, Machine Learning, Computational Linguistics, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Master's with exceptional applied experience also considered.
  • 12+ years of applied data science / ML experience, with at least 3 years in a senior technical leadership role.
  • Demonstrable experience taking AI and ML systems from prototype to production - across multiple problem types (e.g., predictive modeling, NLP, retrieval / RAG, optimization, or agentic workflows).
  • Hands-on proficiency in Python and the modern ML/AI stack (PyTorch, Hugging Face, scikit-learn, and at least one of LangChain / LangGraph or an equivalent agent / LLM framework).
  • Practical experience with at least one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI) and one vector / retrieval stack.
  • Experience designing evaluation frameworks for AI systems - beyond standard benchmarks - including evaluation for foundation-model-based or agentic workflows.
  • Experience operating in a client-facing or cross-functional environment, translating business problems into rigorous data-science programs.
  • *Ideally, you will also have
  • Published research or open-source contributions in AI / ML agentic systems, retrieval, memory and grounding, knowledge graphs, foundation models, optimization, or related areas.
  • Experience with knowledge graphs, ontologies, and neuro-symbolic approaches to grounding and reasoning.
  • Experience building or contributing to a cognitive harness, agent operating system, or agent runtime - internal or open source.
  • Experience with continual learning, online evaluation, drift detection, and model / memory monitoring in production.
  • Familiarity with regulated-industry constraints (SOX, HIPAA, GDPR) and how AI design interacts with audit, retention, and explainability.
  • Prior consulting, product, or hyperscaler experience - comfortable in a fast, ambiguous environment with senior stakeholders.
  • *What we look fo
  • We are looking for a data-science leader who is genuinely excited about hard technical problems and who is as comfortable in a research paper as in a production codebase. You should be the kind of person who treats AI systems as engineered products designed, evaluated, and operated with rigor and who wants to lead a team that ships to Fortune 500 clients across multiple industries.
Responsibilities
  • As a Senior Manager, Data Scientist, you will be a hands-on technical leader who shapes the data-science agenda across multiple workstreams. You will own technical direction, experimentation, and the path from prototype to production for the problems you take on. You will be expected to build, not just design and to lift the bar of the team around you.
  • Lead data-science strategy and execution across multiple AI workstreams including agentic AI, the cognitive harness, memory and grounding, foundation-model applications, machine learning, optimization, and applied analytics.
  • Drive the applied-research and experimentation agenda long-context vs. retrieval trade-offs, hybrid search, reranking, evaluation design, model selection, fine-tuning, and emerging techniques as the field evolves.
  • Design and contribute to the build of core platform components including elements of EY's cognitive harness (memory services, retrieval pipelines, grounding layers, evaluation harnesses) and other reusable AI capabilities.
  • Establish evaluation and quality frameworks for AI systems retrieval and grounding fidelity, hallucination rate, model accuracy, latency, cost, fairness, and continuous evaluation in production.
  • Lead and mentor data scientists and ML engineers set the technical bar, run design reviews, and grow rigor in experimentation, model selection, and production readiness.
  • Partner with sector and functional teams (Finance, Risk, Tax, Supply Chain, HR, Operations) to translate business problems into rigorous, deliverable data-science programs.
  • Engage with clients on complex AI problems shape the technical approach, defend it in front of senior stakeholders, and own delivery quality.
  • Stay abreast of AI technology trends and provide directional guidance and recommendations around models, frameworks, tools, and patterns that fit our clients' existing ecosystems.
  • Apply combined business and technical knowledge to develop and execute target architectures that enable implementation, monitoring, and ongoing improvement of AI at scale.
  • *Skills and attributes for success
  • This role will work to deliver tech at speed, innovate at scale, and put humans at the center. You will provide technical guidance and share knowledge with team members with diverse skills and backgrounds. You will consistently deliver quality client services, focusing on more complex, judgmental, and specialized issues surrounding modern AI, foundation models, and emerging technology. You will demonstrate deep technical capabilities and professional knowledge, and you will lead through making.
  • Strong, hands-on data scientist who codes comfortable building and shipping models, pipelines, services, and experimental frameworks, not just specifying them.
  • Deep technical breadth across modern AI foundation models and prompting, retrieval and grounding, embeddings and fine-tuning, classical ML, optimization, and experimentation rigor.
  • Working knowledge of agentic systems and cognitive harness / agent-runtime architectures - memory, tools, policies, evaluation, observability, cost and the trade-offs between them.
  • Familiarity with at least one major agent SDK (Google ADK, AWS Bedrock AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and the ability to compare them critically.
  • Experience designing and operating production AI systems on cloud platforms (GCP, AWS, Azure, Databricks) including responsible-AI guardrails, monitoring, and continuous evaluation.
  • Track record of leading data-science teams across multiple projects setting technical strategy, mentoring, and raising the bar on rigor and delivery quality.
  • Excellent communication skills able to explain complex AI concepts to executive clients, and able to defend technical choices in front of architects, engineers, and researchers.
  • Comfortable moving between problem spaces - equally credible designing a memory architecture, evaluating a forecasting model, or shaping a new agentic workflow.
View full posting on CareerOneStop → ID: 4f65551cb8e5
Data Scientist - Senior Manager- Consulting - Location OPEN
EY · Portland, OR
Manager Doctorate
2026-05-21
Requirements
  • PhD preferred in Computer Science, Machine Learning, Computational Linguistics, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Master's with exceptional applied experience also considered.
  • 12+ years of applied data science / ML experience, with at least 3 years in a senior technical leadership role.
  • Demonstrable experience taking AI and ML systems from prototype to production - across multiple problem types (e.g., predictive modeling, NLP, retrieval / RAG, optimization, or agentic workflows).
  • Hands-on proficiency in Python and the modern ML/AI stack (PyTorch, Hugging Face, scikit-learn, and at least one of LangChain / LangGraph or an equivalent agent / LLM framework).
  • Practical experience with at least one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI) and one vector / retrieval stack.
  • Experience designing evaluation frameworks for AI systems - beyond standard benchmarks - including evaluation for foundation-model-based or agentic workflows.
  • Experience operating in a client-facing or cross-functional environment, translating business problems into rigorous data-science programs.
  • *Ideally, you will also have
  • Published research or open-source contributions in AI / ML agentic systems, retrieval, memory and grounding, knowledge graphs, foundation models, optimization, or related areas.
  • Experience with knowledge graphs, ontologies, and neuro-symbolic approaches to grounding and reasoning.
  • Experience building or contributing to a cognitive harness, agent operating system, or agent runtime - internal or open source.
  • Experience with continual learning, online evaluation, drift detection, and model / memory monitoring in production.
  • Familiarity with regulated-industry constraints (SOX, HIPAA, GDPR) and how AI design interacts with audit, retention, and explainability.
  • Prior consulting, product, or hyperscaler experience - comfortable in a fast, ambiguous environment with senior stakeholders.
  • *What we look fo
  • We are looking for a data-science leader who is genuinely excited about hard technical problems and who is as comfortable in a research paper as in a production codebase. You should be the kind of person who treats AI systems as engineered products designed, evaluated, and operated with rigor and who wants to lead a team that ships to Fortune 500 clients across multiple industries.
Responsibilities
  • As a Senior Manager, Data Scientist, you will be a hands-on technical leader who shapes the data-science agenda across multiple workstreams. You will own technical direction, experimentation, and the path from prototype to production for the problems you take on. You will be expected to build, not just design and to lift the bar of the team around you.
  • Lead data-science strategy and execution across multiple AI workstreams including agentic AI, the cognitive harness, memory and grounding, foundation-model applications, machine learning, optimization, and applied analytics.
  • Drive the applied-research and experimentation agenda long-context vs. retrieval trade-offs, hybrid search, reranking, evaluation design, model selection, fine-tuning, and emerging techniques as the field evolves.
  • Design and contribute to the build of core platform components including elements of EY's cognitive harness (memory services, retrieval pipelines, grounding layers, evaluation harnesses) and other reusable AI capabilities.
  • Establish evaluation and quality frameworks for AI systems retrieval and grounding fidelity, hallucination rate, model accuracy, latency, cost, fairness, and continuous evaluation in production.
  • Lead and mentor data scientists and ML engineers set the technical bar, run design reviews, and grow rigor in experimentation, model selection, and production readiness.
  • Partner with sector and functional teams (Finance, Risk, Tax, Supply Chain, HR, Operations) to translate business problems into rigorous, deliverable data-science programs.
  • Engage with clients on complex AI problems shape the technical approach, defend it in front of senior stakeholders, and own delivery quality.
  • Stay abreast of AI technology trends and provide directional guidance and recommendations around models, frameworks, tools, and patterns that fit our clients' existing ecosystems.
  • Apply combined business and technical knowledge to develop and execute target architectures that enable implementation, monitoring, and ongoing improvement of AI at scale.
  • *Skills and attributes for success
  • This role will work to deliver tech at speed, innovate at scale, and put humans at the center. You will provide technical guidance and share knowledge with team members with diverse skills and backgrounds. You will consistently deliver quality client services, focusing on more complex, judgmental, and specialized issues surrounding modern AI, foundation models, and emerging technology. You will demonstrate deep technical capabilities and professional knowledge, and you will lead through making.
  • Strong, hands-on data scientist who codes comfortable building and shipping models, pipelines, services, and experimental frameworks, not just specifying them.
  • Deep technical breadth across modern AI foundation models and prompting, retrieval and grounding, embeddings and fine-tuning, classical ML, optimization, and experimentation rigor.
  • Working knowledge of agentic systems and cognitive harness / agent-runtime architectures - memory, tools, policies, evaluation, observability, cost and the trade-offs between them.
  • Familiarity with at least one major agent SDK (Google ADK, AWS Bedrock AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and the ability to compare them critically.
  • Experience designing and operating production AI systems on cloud platforms (GCP, AWS, Azure, Databricks) including responsible-AI guardrails, monitoring, and continuous evaluation.
  • Track record of leading data-science teams across multiple projects setting technical strategy, mentoring, and raising the bar on rigor and delivery quality.
  • Excellent communication skills able to explain complex AI concepts to executive clients, and able to defend technical choices in front of architects, engineers, and researchers.
  • Comfortable moving between problem spaces - equally credible designing a memory architecture, evaluating a forecasting model, or shaping a new agentic workflow.
View full posting on CareerOneStop → ID: 06cb3337d70e
Data Scientist - Senior Manager- Consulting - Location OPEN
EY · Salem, OR
Manager Doctorate
2026-05-21
Requirements
  • PhD preferred in Computer Science, Machine Learning, Computational Linguistics, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Master's with exceptional applied experience also considered.
  • 12+ years of applied data science / ML experience, with at least 3 years in a senior technical leadership role.
  • Demonstrable experience taking AI and ML systems from prototype to production - across multiple problem types (e.g., predictive modeling, NLP, retrieval / RAG, optimization, or agentic workflows).
  • Hands-on proficiency in Python and the modern ML/AI stack (PyTorch, Hugging Face, scikit-learn, and at least one of LangChain / LangGraph or an equivalent agent / LLM framework).
  • Practical experience with at least one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI) and one vector / retrieval stack.
  • Experience designing evaluation frameworks for AI systems - beyond standard benchmarks - including evaluation for foundation-model-based or agentic workflows.
  • Experience operating in a client-facing or cross-functional environment, translating business problems into rigorous data-science programs.
  • *Ideally, you will also have
  • Published research or open-source contributions in AI / ML agentic systems, retrieval, memory and grounding, knowledge graphs, foundation models, optimization, or related areas.
  • Experience with knowledge graphs, ontologies, and neuro-symbolic approaches to grounding and reasoning.
  • Experience building or contributing to a cognitive harness, agent operating system, or agent runtime - internal or open source.
  • Experience with continual learning, online evaluation, drift detection, and model / memory monitoring in production.
  • Familiarity with regulated-industry constraints (SOX, HIPAA, GDPR) and how AI design interacts with audit, retention, and explainability.
  • Prior consulting, product, or hyperscaler experience - comfortable in a fast, ambiguous environment with senior stakeholders.
  • *What we look fo
  • We are looking for a data-science leader who is genuinely excited about hard technical problems and who is as comfortable in a research paper as in a production codebase. You should be the kind of person who treats AI systems as engineered products designed, evaluated, and operated with rigor and who wants to lead a team that ships to Fortune 500 clients across multiple industries.
Responsibilities
  • As a Senior Manager, Data Scientist, you will be a hands-on technical leader who shapes the data-science agenda across multiple workstreams. You will own technical direction, experimentation, and the path from prototype to production for the problems you take on. You will be expected to build, not just design and to lift the bar of the team around you.
  • Lead data-science strategy and execution across multiple AI workstreams including agentic AI, the cognitive harness, memory and grounding, foundation-model applications, machine learning, optimization, and applied analytics.
  • Drive the applied-research and experimentation agenda long-context vs. retrieval trade-offs, hybrid search, reranking, evaluation design, model selection, fine-tuning, and emerging techniques as the field evolves.
  • Design and contribute to the build of core platform components including elements of EY's cognitive harness (memory services, retrieval pipelines, grounding layers, evaluation harnesses) and other reusable AI capabilities.
  • Establish evaluation and quality frameworks for AI systems retrieval and grounding fidelity, hallucination rate, model accuracy, latency, cost, fairness, and continuous evaluation in production.
  • Lead and mentor data scientists and ML engineers set the technical bar, run design reviews, and grow rigor in experimentation, model selection, and production readiness.
  • Partner with sector and functional teams (Finance, Risk, Tax, Supply Chain, HR, Operations) to translate business problems into rigorous, deliverable data-science programs.
  • Engage with clients on complex AI problems shape the technical approach, defend it in front of senior stakeholders, and own delivery quality.
  • Stay abreast of AI technology trends and provide directional guidance and recommendations around models, frameworks, tools, and patterns that fit our clients' existing ecosystems.
  • Apply combined business and technical knowledge to develop and execute target architectures that enable implementation, monitoring, and ongoing improvement of AI at scale.
  • *Skills and attributes for success
  • This role will work to deliver tech at speed, innovate at scale, and put humans at the center. You will provide technical guidance and share knowledge with team members with diverse skills and backgrounds. You will consistently deliver quality client services, focusing on more complex, judgmental, and specialized issues surrounding modern AI, foundation models, and emerging technology. You will demonstrate deep technical capabilities and professional knowledge, and you will lead through making.
  • Strong, hands-on data scientist who codes comfortable building and shipping models, pipelines, services, and experimental frameworks, not just specifying them.
  • Deep technical breadth across modern AI foundation models and prompting, retrieval and grounding, embeddings and fine-tuning, classical ML, optimization, and experimentation rigor.
  • Working knowledge of agentic systems and cognitive harness / agent-runtime architectures - memory, tools, policies, evaluation, observability, cost and the trade-offs between them.
  • Familiarity with at least one major agent SDK (Google ADK, AWS Bedrock AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and the ability to compare them critically.
  • Experience designing and operating production AI systems on cloud platforms (GCP, AWS, Azure, Databricks) including responsible-AI guardrails, monitoring, and continuous evaluation.
  • Track record of leading data-science teams across multiple projects setting technical strategy, mentoring, and raising the bar on rigor and delivery quality.
  • Excellent communication skills able to explain complex AI concepts to executive clients, and able to defend technical choices in front of architects, engineers, and researchers.
  • Comfortable moving between problem spaces - equally credible designing a memory architecture, evaluating a forecasting model, or shaping a new agentic workflow.
View full posting on CareerOneStop → ID: cd7c9bd89325
Technical Program Manager III, Machine Learning, Google Cloud
Google · Kirkland, WA
Manager Doctorate
2026-05-16
Requirements
  • Bachelor's degree in a relevant field, or equivalent practical experience.
  • 5 years of experience in program management.
  • Experience working with data structures or machine learning algorithms.
Preferred
  • Master's degree, PhD, or equivalent experience in Engineering, Computer Science, or other technical related field.
  • 5 years of experience managing cross-functional or cross-team projects.
  • 3 years of experience with machine learning algorithms and tools (e.g. TensorFlow), artificial intelligence, or deep learning.
Responsibilities
  • A problem isn't truly solved until it's solved for all. That's why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you'll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You'll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.
  • Using your extensive technical and leadership expertise, you'll manage projects of various size and scope, identifying future opportunities, improving processes and driving the technical directions of your programs.
  • Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
  • The US base salary range for this full-time position is $163,000-$237,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
  • Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more aboutbenefits at Google (https://careers.google.com/benefits/) .
  • Provide software development and project management, coordination, and inter/intra team communications to deliver outstanding program outcomes.
  • Work closely with Software Engineers, QA, Product Managers and other engineering teams to get high-quality products and features through the software project lifecycle (build, test and release on time).
  • Manage project schedules, identify possible issues and clearly communicate them to project stakeholders.
  • Lead several technical programs for Google Cloud, setting priorities for products and engineering, leading teams to take products to market, assuring success metrics are informing future efforts, and quickly fine tuning the program as needed.
  • Exercise knowledge of data structures or algorithms that improve software performance over time. Build, maintain and enhance business, operational, and management dashboards
  • Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google'sApplicant and Candidate Privacy Policy (./privacy-policy) .
View full posting on CareerOneStop → ID: 87cede07c68e
Senior Machine Learning Manager, Search & Knowledge Platform
Apple · Seattle, WA
Manager Doctorate
2026-05-13
Requirements
  • 8+ years of experience in leading engineering/applied research/ML experiences in natural language processing, SOTA generative AI models
  • Proven record of consistent delivery of technology/products across the full Machine Learning life cycle
  • MS or Ph.D. in Computer Science, Machine Learning, information retrieval, data mining, or a related field
Preferred
  • Strong background and experience in Machine Learning, NLP, and RAG.
  • Strong engineering and R&D experience in LLM post-training, advanced RL-based methods to improve LLM models' safety and quality using RLHF/RLAIF, reward model, advanced RL policy optimization algorithms, cutting-edge hallucination reduction methods, and their engineering implementation, hands-on experience to develop and ship RL based models with high availability, low latency, robustness, and stability.
  • Exceptional verbal and written communication skills to lead
  • Excellent product vision and sound business acumen. Ability to manage long-term strategy and short-term deliverables.
  • Strong engineering leadership and fundamentals.
Responsibilities
  • Apple is where individual imaginations gather together, contributing to the values that lead to great work. Every The AI, Search & Knowledge Platforms team builds amazing products and services for Apple's customers while serving as a foundational partner to teams across Apple. The team delivers world-class AI, search, and knowledge systems powering Siri, Apple Intelligence, Safari, and iMessage, and operates the foundational platforms and infrastructure that keep these intelligent experiences running at hyperscale.
  • You will lead the strong team of MLE, SWE, and data engineers responsible for delivering efficient and effective Generative AI models to build and improve the summarization capabilities across different data types.
  • In this role, you'll drive E2E R&D and engineering to generate high-quality summaries and experiences for Apple users. This includes on-device LLM models for personal content summarization across 1P and 3P apps, and powerful summarization models on Apple's Private Cloud Compute servers. In addition, improve the summarization models' quality for world knowledge-seeking questions and Safari pages to provide accurate answers and highlight web page gists in real-time. Lead the team to develop SOTA LLM-based generative models, groundedness models, and safety models for accurate, grounded, concise, and safe summaries. Develop sophisticated on-device and on-server software frameworks for context integration fast and cost-efficient LLM-based model inference. Integrate the Apple ecosystem with Apple's LLM infrastructure and generative models to deliver delightful user experiences. Devise the product vision and strategy and execute the plan to deliver the highest quality end-user experience. Collaborate with various organizational partners to profoundly impact billions of Apple users worldwide.
View full posting on CareerOneStop → ID: 01ef76150873
Manager, Machine Learning Infrastructure - SIML
Apple · Seattle, WA
Manager Doctorate
2026-05-09
Requirements
  • Bachelor's, Master's, or Ph.D. in Computer Science, Computer Engineering, or a related field (or equivalent experience)
  • 7+ years of software engineering experience, with 2+ years in a technical leadership or management role
  • Strong programming skills in one or more of: Python, Java, Go, C/C++ Solid understanding of machine learning fundamentals and ML system workflows
  • Proven experience in building and scaling distributed systems and backend infrastructure
  • Strong system design skills with expertise in at least one systems domain (e.g., data infrastructure, distributed systems, ML platforms)
Preferred
  • Experience building infrastructure for ML workflows (data pipelines, training systems, evaluation frameworks, or deployment systems)
  • Domain experience in areas such as AI/ML, computer vision, NLP, or related fields
  • Experience working with large-scale datasets and compute-intensive systems
  • Experience improving developer productivity through tooling and platform abstractions
  • Ability to operate effectively in cross-functional, fast-paced environments with evolving requirements
Responsibilities
  • Do you think Computer Vision and Machine Learning can change the world? Do you think it can transform the way millions of people collect, discover and share the most special moments of their lives? We truly believe it can. And we are looking for hardworking engineers who can contribute to building the ecosystem of tooling necessary to create these exciting technologies.
  • We are the System Intelligent and Machine Learning (SIML) group that provides foundational computer vision and machine learning technologies to Apple's ecosystem. Our work is behind essential features such as Camera, Text & Handwriting recognition, and Apple Intelligence experiences (Image Playground, Writing Tools, Smart Script, Math Notes..). We are seeking an Engineering Manager to lead the development of scalable, high-performance infrastructure that powers product-focused machine learning initiatives.
  • In this role you will lead a team responsible for building and operating infrastructure that enables large-scale data processing (terabytes and beyond) across domains such as image generation, large language models (LLMs), computer vision, natural language processing, human-computer interaction, and text recognition. This includes designing systems for dataset creation and management, ingesting annotated and inferred data, and delivering seamless access to high-quality data for ML researchers and engineers.
  • A key part of this role is driving systems that enable deeper understanding of model behavior-such as failure mode analysis, evaluation pipelines, and benchmarking frameworks-to accelerate iteration velocity and improve model quality. You will work across the stack, tackling challenges ranging from low-level distributed systems and compute efficiency to building stable, intuitive interfaces for internal users.
  • As a leader, you will partner closely with cross-functional teams including ML researchers, product teams, and platform engineering to define roadmaps, align priorities, and deliver impactful solutions. You will play a critical role in shaping how ML systems are developed, evaluated, and scaled from early experimentation to production.
View full posting on CareerOneStop → ID: d0fb3fc55c81
Senior Manager, Data Science - Quantum Computing Research (Remote-Eligible)
Capital One · Olympia, WA
Manager Doctorate
2026-05-01
Requirements
  • Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
  • A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 7 years of experience performing data analytics
  • A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 5 years of experience performing data analytics
  • A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 years of experience performing data analytics
  • At least 2 years of experience leveraging open source programming languages for large scale data analysis
  • At least 2 years of experience working with machine learning
  • At least 2 years of experience utilizing relational databases
Preferred
  • Ph.D. in Physics, Computer Science, Mathematics, or a related field with a strong focus on quantum information or quantum computing.
  • At least 7 years of experience in quantum computing research and development.
  • At least 7 years of experience partnering with quantum hardware developers to implement and evaluate algorithms.
  • At least 7 years of experience in quantum algorithms (e.g. Shor's algorithm, Grover's algorithm, Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA)).
  • At least 7 years of experience in quantum information theory and quantum computing applied to Machine Learning.
  • Excellent verbal and written communication skills with the ability to effectively communicate technical advances and strategy to research scientists, engineering teams, senior executives, and non-technical audiences.
  • Knowledge of advanced quantum hardware and their associated control systems.
  • Experience with large-scale classical simulation of quantum systems (e.g., with tensor networks or state-vector simulators).
  • Experience with production-level quantum hardware or cloud-based quantum services.
  • Worked with datasets or systems involving 100+ qubits.
  • Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
  • Capital One is open to hiring a Remote Employee for this opportunity.
  • The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
  • Remote (Regardless of Location): $209,000 - $238,500 for Sr Mgr, Data Science
  • McLean, VA: $229,900 - $262,400 for Sr Mgr, Data Science
  • Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
  • This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
  • Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website (https://www.capitalonecareers.com/benefits) . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
  • This role is expected to accept applications for a minimum of 5 business days.
View full posting on CareerOneStop → ID: 9ee44c336ec7
(USA) Senior Manager, Data Science
Walmart · Bellevue, WA
Manager Doctorate
2026-04-23
Requirements
  • Deep understanding of machine learning, statistical modeling, and data science techniques used for risk mitigation in e-commerce or marketplace environments.
  • Proven ability to build, deploy, and optimize complex data science models to identify and mitigate fraud, performance, and operational risks.
  • Proficiency in tools and languages such as Python, R, Spark, Scala , and machine learning frameworks (e.g., TensorFlow, PyTorch, XGBoost) to develop and deploy risk models.
  • Ability to understand the end-to-end risk management process, from data ingestion and feature engineering to model deployment and real-time decision making.
  • 5-8 years of experience in leading teams or projects related to data science, including mentoring junior data scientists and guiding technical teams toward best practices in model development and deployment.
  • Comfortable navigating complex and uncertain situations, making data-driven decisions to improve risk management strategies in a fast-evolving environment.
  • Strong ability to translate complex data science concepts into clear, actionable insights for non-technical stakeholders across the organization.
  • Understanding how data science and risk management intersect with broader business objectives and the ability to align risk strategies with organizational goals.
  • Option 1 : Bachelor's degree in Statistics, Computer Science, Data Science, Mathematics, or related field, with 5-8 years of hands-on experience in data science, machine learning, or risk management.
  • Option 2 : Master's degree in a related field (e.g., Data Science, Machine Learning, Statistics, Applied Mathematics) with at least 3-5 years of applied experience working on data-driven risk management or fraud prevention.
  • Option 3 : 8-10 years of direct experience in data science, machine learning, or applied risk management within an e-commerce or marketplace setting.
  • _Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications._
  • Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics related field. Option 3: 7 years' experience in an analytics or related field.
Preferred
  • Expertise in using advanced machine learning techniques such as deep learning, reinforcement learning, or anomaly detection for fraud detection or risk mitigation.
  • Experience with big data technologies like Apache Spark , Hadoop , and cloud-based data solutions (e.g., AWS, Google Cloud) to build scalable risk management platforms.
  • Proficiency in data manipulation and analysis tools such as Pandas, NumPy , and SQL for data wrangling, feature engineering, and analysis.
  • Strong background in model evaluation techniques including ROC/AUC, confusion matrices, precision/recall, and F1 scores, as well as experience with A/B testing and model validation
  • _Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications._
  • Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch), We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart's accessibility standards and guidelines for supporting an inclusive culture.
  • Masters: Business Administration, Masters: Information Systems, Masters: Statistics
  • *Primary Location...
  • 10900 Ne 4th St, Bellevue, WA 98004, United States of America
  • Walmart and its subsidiaries are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.
Responsibilities
  • The Senior Manager, DataScience will lead a team of data scientists to define, implement, test, and deploy decision strategies aimed at mitigating fraud and performance risks for Walmart Marketplace. In this role, you will work closely with cross-functional teams, including product, engineering, and data science, to continuously monitor, investigate, and respond to emerging risk trends. You'll be responsible for leveraging advanced data science methodologies to develop and refine risk management models, ensuring the strategies are effective and scalable across both domestic and international portfolios.
  • *How You'll Make an Impact:
  • Drive Data Science Innovation to protect the integrity of the Marketplace by applying advanced statistical methods, machine learning, and AI techniques to identify and mitigate fraud and performance risks.
  • Support Marketplace Growth by designing and implementing scalable, data-driven risk management solutions that align with key business objectives and growth targets.
  • Provide technical leadership and mentorship to your team, overseeing the development of decision models, managing model performance, and ensuring they are optimized for both accuracy and scalability.
  • Apply Advanced Data Science Techniques such as predictive modeling, supervised and unsupervised machine learning, deep learning, and anomaly detection to continuously improve risk strategies.
  • Collaborate Across Teams to integrate data science models with business processes, ensuring alignment between product, engineering, and data teams to address key risk areas effectively.
  • Monitor the performance of deployed models, identify opportunities for improvement, and iterate to enhance their predictive power and robustness in mitigating risks.
  • Develop Test & Measurement Frameworks to validate model effectiveness, utilizing rigorous A/B testing, statistical testing, and model evaluation to refine decision strategies.
  • Foster Innovation by exploring cutting-edge data science techniques, identifying opportunities to optimize decision-making, and driving improvements in risk management capabilities.
View full posting on CareerOneStop → ID: 4bb83fb26716
Manager- Applied Sciences / Machine Learning
Microsoft Corporation · Redmond, WA
Manager Doctorate
2026-04-03
Requirements
  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
  • 3+ years of people management experience.
Preferred
  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
  • 8+ years of industry experience in software engineering and/or machine learning, with prior experience leading teams or technical leadership roles.
  • Solid hands-on background in machine learning, including LLMs, NLP, or recommendation systems.
  • Proven track record of delivering large-scale, production-grade ML systems.
  • Experience leading or owning critical projects in recommendation systems or AIGC scenarios.
  • Proficiency in programming languages such as C/C++, C#, Java, and/or Python.
  • Demonstrated experience managing and growing ML teams, including performance management and career development.
  • Solid expertise in deep learning frameworks such as TensorFlow or PyTorch.
  • Experience with LLM fine-tuning, evaluation, and real-world product deployment.
  • Experience leading projects through full product lifecycle, from concept to launch and iteration.
  • Background in distributed systems and large-scale data processing.
  • Solid foundation in data structures, algorithms, and system design.
  • Experience with large-scale data analytics tools such as Spark.
Responsibilities
  • Lead and grow a team of Applied Scientists and Machine Learning Engineers, including hiring, coaching, and developing talent across Applied Science and engineering.
  • Define technical vision and strategy for recommendation systems, Artificial Intelligence Generated Content (AIGC), and LLM-powered content generation.
  • Drive end-to-end execution across multiple initiatives, from ideation and design to production and iteration.
  • Oversee system architecture and scalability, ensuring robust, efficient, and high-quality ML solutions in production.
  • Partner cross-functionally with product, engineering, and leadership teams to align on priorities and deliver customer impact.
  • Champion innovation in AIGC applications, ranking, and recommendation algorithms.
  • Mentor and elevate the team, fostering a culture of technical excellence, collaboration, and continuous learning.
  • Communicate progress, insights, and strategy to senior leadership and stakeholders.
View full posting on CareerOneStop → ID: edd9ebd3d35f
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