DATA SCIENTIST I–FINANCIAL & TIME SERIES FORECASTING

About the role

We are seeking a driven Data Scientist to join our evolving and dynamic team. This entry-level position is ideal for candidates with a strong mathematical foundation and a passion for predictive modeling. You will build, validate, and maintain multi-variable time series forecasting models to project financial data and agency expenditures over multi-year horizons. Responsibilities include owning the data lifecycle from scraping and transforming raw datasets to rigorous model validation. We value resourceful problem-solvers who adapt quickly to changing data environments and ensure predictive tools remain accurate and resilient.

Responsibilities

• Support development and evaluation of statistical and time series forecasting models, including ARIMA, Prophet, regression, and tree-based models.
• Write and maintain Python scripts to scrape, extract, clean, join, and transform structured and unstructured financial data from web sources, APIs, databases, and raw files.
• Execute established validation workflows, compare model performance using metrics such as RMSE, MAE, and MAPE, and identify potential data-quality or data-leakage issues.
• Assist with analyzing historical pricing, inflation indices, budget cycles, and spending patterns to create features for forecasting models.
• Perform exploratory data analysis and document data sources, assumptions, transformations, model results, and known limitations.
• Create reports, visualizations, and summaries that explain analytical findings to technical and non-technical stakeholders.
• Learn new domain requirements, proprietary databases, customer tools, and forecasting methods with guidance from senior team members.
• Maintain strict compliance with defense security protocols, confidentiality guidelines, and internal data handling policies.

Basic Qualifications

• US Citizenship required; ability 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.
• Recent graduates and candidates with up to two years of relevant professional, internship, research, or academic experience are encouraged to apply.
• Foundational knowledge of linear algebra, calculus, probability, and statistics.
• Working knowledge of Python for data manipulation and analysis, including pandas and NumPy.
• Academic, internship, research, or project experience involving statistical modeling, machine learning, predictive analytics, or time series forecasting.
• Basic understanding of single-variable and multi-variable forecasting methods.
• Understanding of model evaluation concepts, including training and test datasets, error metrics, overfitting, and data leakage.
• Strong attention to detail and the ability to organize, document, and communicate analytical work.
• Strong verbal and written communication skills with the ability to explain technical concepts clearly.
• 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

• Coursework, internship, research, or project experience in Economics, Finance, Econometrics, Accounting, or time series analysis.
• Experience with scikit-learn, statsmodels, Prophet, matplotlib, or similar analytical libraries.
• Familiarity with SQL, APIs, web scraping, ETL processes, or data-processing tools.
• Experience completing a capstone, thesis, internship, or personal project involving forecasting or predictive modeling.
• Exposure to government, defense, budgeting, or financial datasets.
• Familiarity with inflation adjustments, budget cycles, fiscal years, and macroeconomic factors.
• Familiarity with version control tools such as Git or GitHub.
• Continued education and interest in current and emerging AI/ML technologies.
• Strong problem-solving skills and the ability to work in a fast-paced environment.