OPERATIONS RESEARCH ANALYST

You will coordinate the development and management of data science and artificial intelligence/machine learning projects, including objectives, project plans, schedules, and support requirements.

You will translate naval safety questions and operational needs into analytical studies, determine data collection requirements, select and adapt methodologies, organize project phases, and derive conclusions and recommendations.

You will design and implement relational or dimensional data models and repeatable data pipelines to extract, integrate, clean, transform, and validate data for analysis.

You will develop, test, and document reusable analytical code using tools such as Python and SQL in environments such as Databricks.

You will evaluate and coordinate requests for data science and machine learning analysis, advise stakeholders on feasibility, and adapt analytical approaches or propose alternative data sources to address data limitations and time constraints.

You will develop reports of analytical findings and model conclusions, including charts, tables, dashboards, and presentations.

You will provide recommendations that help technical and nontechnical audiences, including senior leaders, make informed safety and risk decisions

You will initiate and conduct complex research on safety-related topics, including those with limited precedent, and develop, train, and validate machine learning models for prediction, classification, and risk analysis.

You will use statistical analysis and appropriate evaluation methods to assess model performance, assumptions, uncertainty, and limitations.

In addition to the Basic Requirements for this position, your resume must also demonstrate at least one year of specialized experience equivalent to the next lower grade level (GS-12) or pay band in the federal service or equivalent experience in the private or public sector as a professional Operations Research Analyst performing the following duties: 1.

Advanced Modeling - Experience independently developing, validating, and applying machine learning models for prediction, anomaly detection, or risk analysis, including evaluating model performance, assumptions, and limitations. 2.

Programming & Pipelines - Professional experience performing hands-on data engineering and exploratory data analysis using languages such as Python and SQL including building ETL/data pipelines to ingest, integrate, clean, validate, and transform data for analysis and machine learning. 3.

Experience leading or substantially contributing to data science project lifecycles, including defining analytical objectives, identifying data requirements, coordinating data integration, addressing data quality, and documenting analytical methods, model validation results, and recommendations. 4.

Experience designing interactive dashboards (e.g., Qlik, Tableau, PowerBI) and presenting highly technical model outputs, limitations, and predictive insights to Flag-level leadership.

Additional qualification information can be found from the following Office of Personnel Management website: https://www.opm.gov/policy-data-oversight/classification-qualifications/general-schedule-qualification-standards/#url=List-by-Occupational-Series Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., professional, philanthropic, religious, spiritual, community, student, social).

Volunteer work helps build critical competencies, knowledge, and skills and can provide valuable training and experience that translates directly to paid employment.