Job Matches
Washington job postings for occupations your coursework prepares you for. Sorted by how many of the occupation's tasks your courses cover.
Showing CareerOneStop postings from the last 30 days; the scraper re-syncs from CareerOneStop nightly. Last posting in this list dated 2026-06-06.
Scraper last re-confirmed a WA posting 2026-06-06 02:56 (113 touched today).
- Mentor others on mathematical techniques.
- Maintain knowledge in the field by reading professional journals, talking with other mathematicians, and attending professional conferences.
- Develop new principles and new relationships between existing mathematical principles to advance mathematical science.
-
Disseminate research by writing reports, publishing papers, or presenting at professional conferences.
via MATH 491
- Assemble sets of assumptions, and explore the consequences of each set.
Biostatistician, HEOR Consulting Services Company: Norstella Location: Remote, United States Date Posted: May 20, 2026 Employment Type: Full Time Job ID: R-1976 Description About Norstella Norstella is a premier and critical global life sciences data and AI solutions platfor
- Advanced degree (MS or PhD) in Biostatistics, Statistics, or a related quantitative field.
- At least 3 years of professional experience as a biostatistician/statistician using real-world data or clinical trial data in the pharmaceutical, biopharmaceutical, medical device, or CRO/consulting environment.
- Strong proficiency in statistical programming using R (technical proficiency will be tested). Additional proficiency in SQL or Python is valued.
- Ability and willingness to train and work within the Panalgo IHD analytics platform.
- Norstella's HEOR Services team is seeking an experienced biostatistician to develop statistical analysis plans and perform advanced statistical analysis on non-interventional observational research st… Norstella's HEOR Services team is seeking an experienced biostatistician to develop statistical analysis plans and perform advanced statistical analysis on non-interventional observational research studies using real world data. This role will advise internal and external clients on recommended statistical approaches to support study research objectives, and will be directly responsible for documentation, implementation, and interpretation of statistical analysis plans. The ideal candidate brings deep expertise with realworld data, strong R programming skills, and a track record of applying advanced statistical methods to support regulatory, epidemiology, HEOR, and RWE use cases.
- Support senior HEOR leaders with non-interventional observational research study design, advanced statistical analysis, and interpretation using NorstellaLinQ Realworld Data to support regulatory, epi… Support senior HEOR leaders with non-interventional observational research study design, advanced statistical analysis, and interpretation using NorstellaLinQ Realworld Data to support regulatory, epidemiology, HEOR, and RWE use cases.
- During study design phase, recommend appropriate statistical methods to address study research objectives (e.g., power calculations, methods to mitigate selection bias, development of matched cohorts,… During study design phase, recommend appropriate statistical methods to address study research objectives (e.g., power calculations, methods to mitigate selection bias, development of matched cohorts, development of measures from multiple sources [e.g., EMR + claims], analysis of NLP-derived measures from unstructured EMR, approach for outcomes that require time-to-event analysis with competing risk, multivariable modeling, repeated measures, time-averaged clinical measures, causal inference, and techniques for analyzing rare and ultra rare populations including Bayesian statistics).
- Write statistical analysis plans to address the above considerations.
- Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
- Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
- Report results of statistical analyses, including information in the form of graphs, charts, and tables.
- Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
- Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
- Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
- Report results of statistical analyses, including information in the form of graphs, charts, and tables.
- Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
- Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
These could be filled by an applied project, elective, or internship — see the program page for examples.
We made history and now we work to transform the future - for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters. Do you believe data tel
- Accelerate the application of value-added analytics and machine learning into the portfolio of products for Manufacturing Analytics.
- Drive analytic excellence into product teams by collaborating with Data Scientists, Data Engineers and Software Engineers in analytic and machine learning methods.
- Work closely with the Product Manager and Product Owner to translate Business Value needs into analytic deliverables and, where appropriate, software products for delivery by product teams.
- Work hands-on with the team and other partners to deliver solutions that meet our customer's requirements and needs.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Analyze, manipulate, or process large sets of data using statistical software.
-
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
via CSCD 484
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Clean and manipulate raw data using statistical software.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Present the results of mathematical modeling and data analysis to management or other end users.
- Define data requirements, and gather and validate information, applying judgment and statistical tests.
- Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.
-
Prepare management reports defining and evaluating problems and recommending solutions.
via MATH 491
-
Collaborate with others in the organization to ensure successful implementation of chosen problem solutions.
via CSCD 210
These could be filled by an applied project, elective, or internship — see the program page for examples.
- Plan, coordinate, and implement security measures to safeguard information in computer files against accidental or unauthorized damage, modification or disclosure.
-
Test programs or databases, correct errors, and make necessary modifications.
via CSCD 210
-
Train users and answer questions.
via CSCD 484
-
Develop standards and guidelines for the use and acquisition of software and to protect vulnerable information.
via MATH 491
-
Write and code logical and physical database descriptions and specify identifiers of database to management system, or direct others in coding descriptions.
via CSCD 300
These could be filled by an applied project, elective, or internship — see the program page for examples.