Principal Quantitative Engineer, Investments Technology

Lead the design and implementation of applications, proofs of concept, and production-grade tooling that advance portfolio construction, asset allocation analytics, and quantitative research workflows. Partner with quantitative analysts and portfolio strategists to convert AI-generated prototypes and research artifacts into robust, production-ready applications. Build and maintain data pipelines for portfolio and market data across public and private markets, leveraging the LMI data ecosystem. Contribute to the team's shared quantitative library, supporting both existing and new models, analytics, and reusable components. Support and extend existing applications and dashboards in production, and drive the design and implementation of new ones to meet evolving research and decision-support needs. Excellent problem-solving and analytical 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 using Agentic Programming tools (Github Copilot, Claude) required. Experience developing applications for investment management firms or, more broadly, financial services is a strong plus. Strong SQL skills required. Familiarity with financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques strongly preferred. Strong quantitative background required, with hands-on experience applying statistical, time-series, and optimization techniques using Python libraries (e.g., SciPy, Scikit-Learn, cvxpy, statsmodels) to portfolio construction and asset allocation problems. Deep understanding of investment management and portfolio composition strategies, with knowledge across asset classes including fixed income, public and private equity, and private credit. Demonstrated experience applying quantitative methodologies (including optimization, factor modeling, risk decomposition, and econometrics) to support portfolio construction, asset allocation, and investment decisions. Demonstrated ability to partner with business teams to convert AI-generated artifacts (e.g., prototypes, proofs of concept) into production-ready applications. Experience bridging the gap between rapid AI-developed prototypes to quality full-stack applications is highly valued. Proven ability to design, build, and scale application systems in data-rich environments including custom AI tools. Master's or Ph.D. (or equivalent experience) in a quantitative or engineering field such as Computer Science, Mathematics, Statistics, Econometrics, Financial Engineering, or a related discipline. Eight or more years of software engineering experience A Bachelor's or Master's degree in a technical or business discipline, or equivalent experience