About this role
Working directly with the PSM team and the broader Private Markets organization, you will focus on enhancing day-to-day AI use cases, helping turn prototypes and POC artifacts into robust systems and tools that scale into production-ready applications. Embed with the PSM team to understand portfolio strategy, pacing, liquidity, and performance workflows, and translate them into well-scoped, iteratively delivered tools. Design and build full-stack Python applications, data pipelines, and decision-ready reports on enterprise platforms (GitHub, CI/CD via GitHub Actions, Snowflake, Azure AD-secured web apps).
Help analysts take AI-generated prototypes and proofs of concept and harden them into reliable, maintainable, institutional-grade applications. Implement output validation, documented assumptions and limitations, and basic runtime logging for decision-support tools; provide best-effort operational support and remediate vulnerabilities within enterprise SLAs. Maintain clean, well-organized repositories: consistent project structure, automated linting and formatting, semantic version bumps and tagged releases, and up-to-date documentation so every solution stays maintainable and auditable. Contribute to and reuse shared utilities (connectors, authentication helpers, report distribution) in collaboration with other embedded engineers.
- Excellent problem-solving skills, with the ability to think critically, independently, and act with minimal handholding.
- Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders.
- Strong attention to detail, organization, and the ability to manage multiple tasks and priorities in a fast-paced environment.
- Full-stack development knowledge with a minimum of 5 years professional experience programming in Python demonstrating the ability to write efficient and robust code able to process and analyze large financial datasets.
- Experience with key Python Libraries (pandas, NumPy) required.
- Experience in front-end development and user experience (UX) design required; experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash) preferred.
- Experience using Version Control (Git) required; experience with CI/CD workflows (e.g., GitHub Actions) and cloud deployment preferred.
- Experience using Agentic Programming tools (GitHub Copilot, Claude) required.
- Demonstrated ability to partner with business teams to convert AI-generated artifacts (e.g., prototypes, proofs of concept) into institutional-grade, production-ready applications.
- Experience bridging the gap between rapid AI-assisted development and enterprise-quality software delivery is highly valued.
- Proven ability to design, build, and scale application systems in data-rich environments including custom AI tools.
- Experience developing applications for investment management firms or, more broadly, financial services is a strong plus.
- Strong SQL skills required.
- Experience with Snowflake is a plus.
- Familiarity with private markets data platforms (such as eFront, PitchBook, DealCloud) and data manipulation techniques strongly preferred.
- Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy) is preferred; experience with Monte Carlo or scenario-based cash-flow modeling is a plus.
- Solid understanding of private markets, including fund structures, capital calls and distributions, J-curve dynamics, commitment pacing, and performance measurement (IRR, MOIC/TVPI, DPI, PME); foundational knowledge across private equity, private credit, real estate, and infrastructure.
- Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics.
- 8+ years of experience Bachelor's degree or equivalent experience