OPERATIONS RESEARCH ANALYST

US Immediate Office of the Chief of Naval Operations; Norfolk (VA); On-site; USD 90,000 - 140,000; Full time

Job summary

US Navy personnel seeking a data science specialist leverages AI/ML modeling, Python/SQL, and data pipelines to deliver actionable insights and governance across operational systems. The ideal candidate demonstrates experience in cross‑agency collaboration, dashboards, and clear communication to senior leaders.

Strong emphasis on statistical reasoning, operations research, and delivering well-documented, reusable code suitable for high‑level decision-making.

Qualifications

• Experience with AI/ML modeling and evaluating models.
• Hands-on data wrangling and building ETL/data pipelines in Python/SQL.
• Experience across data science life-cycles and cross‑agency data integration.
• Ability to design interactive dashboards and present results to senior leaders.

Skills

Python programming

SQL querying

Statistical reasoning

Operations research

Tools

Databricks

Apache Spark/PySpark

PowerBI/Tableau

Job description

GS 13: In addition to the basic requirement your resume must also demonstrate at least one year of specialized experience at or equivalent to the GS-12 grade level or pay band in the Federal service or equivalent experience in the private or public sector. Specialized experience must demonstrate the following:

• 1) Coordinating the development and management of data science or artificial intelligence/machine learning (AI/ML) projects, including project justification, objectives, project plans, schedules, and support requirements with analysts, engineers, commanders, staff representatives, agency executives, customers, and appropriate organizations;
• 2) Planning and conducting data science projects, including translating operational questions into analytical problems, selecting appropriate methods, organizing project phases, identifying data requirements, collecting and analyzing data, and developing conclusions and recommendations;
• 3) Implementing relational or dimensional data models and repeatable data pipelines to extract, integrate, clean, transform, and validate data, using documented and tested analytical code and tools or environments such as Python, SQL, Databricks, Apache Spark/PySpark, or comparable technologies;
• 4) Developing, adapting, and evaluating machine learning methods and models for complex research involving safety, risk, or comparable operational problems, including problems with limited precedent. Relevant activities may include feature engineering, model training, hyperparameter tuning, model validation, and comparison of model performance, assumptions, and limitations using appropriate statistical and evaluation methods;
• 5) Evaluating and coordinating requests for AI/ML analysis, advising stakeholders on feasibility, and adjusting analytical approaches, data requirements, model inputs, or project scope to address available data, methodological limitations, and time constraints;
• 6) Developing reports, charts, tables, dashboards, and presentations that explain analytical methods and model results, and communicating defensible conclusions, recommendations, assumptions, and limitations to technical and nontechnical audiences, including senior leaders.

GS-12: In addition to the basic requirement your resume must also demonstrate at least one year of specialized experience at or equivalent to the GS-11 grade level or pay band in the Federal service or equivalent experience in the private or public sector. Specialized experience must demonstrate the following:

• 1) AI/ML Advanced Modeling- Assisting with designing and deploying machine learning models, simulation-based forecasts, or risk models to identify trends, anomaly patterns, or high-risk areas within operational systems;
• 2) Programming & Pipelines- performing hands‑on data wrangling, building ETL/data pipelines, and conducting exploratory analysis using programming languages such as Python, R, and SQL in environments such as Databricks;
• 3) Experience in being a part of data science life-cycles, including defining analytical objectives, coordinating cross‑agency data integration, managing data governance, and authoring formal model validation reports;
• 4) Experience designing interactive dashboards (e.g., Qlik, Tableau, PowerBI) and presenting highly technical model outputs, limitations and predictive insights to Flag-level leadership.

GS-11: In addition to the basic requirement your resume must also demonstrate at least one year of specialized experience at or equivalent to the GS-09 grade level or pay band in the Federal service or equivalent experience in the private or public sector. Specialized experience must demonstrate the following:

• 1) AI/ML Advanced Modeling- Assisting with designing and deploying machine learning models, simulation-based forecasts, or risk models to identify trends, anomaly patterns or high-risk areas within operation systems;
• 2) Programming & Pipelines- aiding with hands‑on data wrangling, building ETL/data pipelines; and conducting exploratory analysis using various programming techniques;
• 3) Participating in data science life-cycles, including assisting with defining analytical objectives, aiding with cross‑agency data integrations and managing data governance;
• 4) Following prescribed procedures to design interactive dashboards, (e.g., Qlik, Tableau, PowerBI) and participating in presentations.

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/1500/operations-research-series-1515/

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.

IDEAL CANDIDATE:

The ideal candidate combines practical AI/ML and data science experience with strong statistical reasoning, operations research skills, and programming ability. They have demonstrated experience using Python and SQL to analyze complex datasets, develop and evaluate machine learning models, and create reusable, tested, and well-documented code. They can independently plan and complete analytical projects, choose appropriate methods for the available data, and communicate findings and recommendations clearly to operational subject matter experts and senior leaders.Experience with data engineering for analytics and machine learning is desirable, including building data pipelines and extract, transform, and load (ETL) workflows; integrating data from multiple sources; validating data quality; and implementing relational or dimensional data models. Experience with Databricks, Apache Spark/PySpark, and Delta Lake, or comparable technologies, is particularly relevant.