GXM is seeking a highly motivated and analytically rigorous
Data Scientist with deep experience in
Private Equity and Private Credit risk and performance analysis to support a high-priority federal investment program focused on accelerating the development and deployment of emerging technologies critical to national and economic security.
This role combines advanced quantitative analysis, alternative investment evaluation, and risk modelling to support senior government stakeholders in making informed capital allocation decisions and to report on program performance. The selected candidate will work at the intersection of finance, data science, and national strategic priorities, analyzing complex investment structures, portfolio performance, and risk indicators across a broad range of innovative technology sectors.
The ideal candidate brings strong expertise in private markets investing, financial modeling, performance and risk analytics, and data-driven decision support, along with the ability to operate effectively in a fast-paced, high-visibility federal environment. This individual will also mentor junior analysts, contribute to cross-functional collaboration, and help improve investment evaluation systems and analytical workflows.
This position is based in the Washington, DC metropolitan area and requires on-site presence five days per week.
Key Responsibilities- Lead advanced quantitative analysis of private equity funds, private credit portfolios, portfolio companies, and alternative investment structures.
- Perform risk and performance analysis across investment portfolios, including credit risk, valuation trends, liquidity exposure, portfolio concentration, and capital deployment effectiveness.
- Develop and maintain analytical models to assess investment opportunities, portfolio health, and long-term strategic outcomes.
- Evaluate emerging technology sectors and companies for alignment with federal investment priorities, including dual-use, commercial, and defense-related applications.
- Produce investment memorandums, executive briefings, dashboards, and data visualizations for senior government leadership and stakeholders.
- Apply advanced data science, statistical modeling, machine learning, and predictive analytics techniques to large structured and unstructured datasets.
- Develop automated workflows and scalable analytical processes to support investment analysis and reporting functions.
- Contribute to the development and refinement of investment policies, reporting frameworks, governance processes, and risk mitigation strategies.
- Identify trends and risks indicators across investment and technology datasets to support strategic decision-making.
Requirements
- U.S. Citizen required and will be verified in-person; no clearance required.
- Bachelor's or Master's degree in Data Science, Finance, Economics, Statistics, Business Analytics, or a related quantitative field.
- 5-7 years of experience in private equity, private credit, investment analytics, quantitative finance, or related financial analytical roles.
- Experience performing private equity and private credit risk and performance analysis, including portfolio analytics, valuation assessment, and investment risk evaluation.
- Experience developing dashboards and visualizations using Power BI, Tableau, or similar tools.
- Strong analytical, statistical, and financial modeling skills with the ability to communicate findings to executive stakeholders.
- Experience working in fast-paced, high-visibility environments with cross-functional teams and senior leadership.
Desired Qualifications - Experience supporting federal investment, innovation, economic development, or technology modernization initiatives.
- Knowledge of emerging technology sectors such as artificial intelligence, advanced manufacturing, semiconductors, energy, autonomy, cybersecurity, biotechnology, or defense technologies.
- Familiarity with federal investment governance, public-private partnerships, or interagency coordination environments.
- Experience applying machine learning or AI techniques to financial forecasting, investment screening, or risk analysis.
- Understanding of technology commercialization and transition challenges, including operational adoption and scaling constraints.
