DATA SCIENTIST I–FINANCIAL & TIME SERIES FORE

VSolvit•Norco, CA

1d•$35 - $46

About The Position

We are seeking a talented and driven Data Scientist to join our evolving and dynamic team. This position is open to candidates at entry 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

Support the 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.