*100% ON-SITE Norco, CA*Job SummaryWe are seeking a talented and driven Data Scientist to join our evolving and dynamic team. This position is open to candidates at Mid to Senior level of experience who possess a strong mathematical foundation and a passion for predictive modeling. In this role, you will build, validate, and maintain multi-variable time series forecasting models to project financial data and agency expenditures over multi-year horizons. You will work directly with financial records, taking ownership of the data lifecycle, from scraping, joining, and transforming raw datasets to rigorous model validation. We are looking for a resourceful problem-solver who excels at mathematical modeling, adapts quickly to changing data environments, and ensures our predictive tools remain accurate and resilient.
As with any position, additional expectations exist. Some of these are, but are not limited to, adhering to normal working hours, meeting deadlines, following company policies as outlined by the Employee Handbook, communicating regularly with assigned supervisor(s), and staying focused on the assigned tasks including company meetings, and completing other tasks as assigned.
Responsibilities- Time Series & Mathematical Modeling: Independently develop, compare, validate, and maintain advanced single-variable and multi-variable forecasting models using methods such as ARIMA, Prophet, regression, exponential smoothing, and tree-based models.
- Data Pipeline Construction & Scripting: Design and maintain modular Python scripts and data pipelines to scrape, extract, clean, join, validate, and transform structured and unstructured financial data.
- Model Validation & Quality Assurance: Design and execute backtesting, time-based cross-validation, regression testing, and model-monitoring workflows to ensure accuracy when code or data inputs change.
- Quantitative Feature Engineering: Analyze historical pricing, inflation indices, economic indicators, budget cycles, and spending patterns to develop statistically and economically defensible features.
- Model Performance & Risk Analysis: Evaluate forecast performance using RMSE, MAE, MAPE, bias, benchmark comparisons, and prediction intervals while preventing data leakage and look-ahead bias.
- Technical Ownership: Document model assumptions, limitations, data dependencies, validation results, and recommended uses of model outputs.
- Stakeholder Communication: Translate complex modeling results into clear reports, visualizations, recommendations, and leadership briefings.
- Technical Guidance: Provide technical support, code reviews, and guidance to junior Data Scientists or analysts as assigned.
- Resourcefulness & Learning: Evaluate new forecasting, statistical, AI, and machine-learning approaches and recommend improvements to analytical workflows.
- Compliance & Security: Maintain strict compliance with defense security protocols, confidentiality guidelines, and internal data handling policies.
Basic QualificationsUS Citizenship RequiredAbility to obtain and maintain a Secret Security Clearance- Bachelor's degree in Mathematics, Statistics, Computer Science, Data Science, Economics, Finance, Engineering, or a related quantitative field and at least three years of relevant professional experience.
- A Master's or Ph.D. degree in a related quantitative field may substitute for a portion of the professional experience requirement.
- Strong mathematical foundation in linear algebra, calculus, probability, statistics, and hypothesis testing.
- Professional proficiency in Python for data manipulation, statistical modeling, time series forecasting, and automation scripting.
- Demonstrated professional experience developing and validating predictive or time series forecasting models.
- Demonstrated knowledge of both single-variable and multi-variable forecasting methods.
- Experience with backtesting, time-based cross-validation, model benchmarking, and error-metric evaluation.
- Strong understanding of data leakage, look-ahead bias, overfitting, feature stability, and model uncertainty.
- Experience developing clean, modular, documented, and maintainable analytical code.
- Ability to independently investigate data-quality, pipeline, or model-performance issues.
- Strong verbal and written communication skills with the ability to explain mathematical models, assumptions, risks, and results to non-technical stakeholders.
- If applicable: If you are or have been recently employed by the U.S. government, a post-employment ethics letter will be required if employment is offered.
Preferred Skills and Qualifications- Master's or Ph.D. degree in Mathematics, Statistics, Computer Science, Data Science, Economics, Finance, Engineering, or a related quantitative field.
- Professional experience in Economics, Finance, Econometrics, Quantitative Accounting, financial planning, or budget forecasting.
- Experience forecasting financial expenditures, pricing trends, inflation-adjusted costs, or multi-year budget requirements.
- Experience with scikit-learn, statsmodels, Prophet, XGBoost, LightGBM, or similar modeling libraries.
- Experience with SQL, APIs, web scraping, ETL processes, databases, and automated data pipelines.
- Experience working with government financial data or in a defense-related environment.
- Experience with automated testing, model monitoring, reproducible analytical workflows, or production model deployment.
- Experience with DevOps practices, Git, GitHub, CI/CD pipelines, containers, or cloud environments.
- Experience preparing technical documentation, customer briefings, and leadership presentations.
- Experience reviewing code or mentoring junior technical staff.
- Continued education and knowledge of current and emerging AI/ML technologies.
- Strong problem-solving skills and the ability to work independently in a fast-paced environment.
