Helping construct multi-asset and single asset portfolios using advanced asset allocation and portfolio construction techniques
Building high performance computing algorithms, infrastructure and APIs
Researching and building prediction and forecasting methods based on both classical statistical techniques as well as ML based techniques including Agentic workflows leveraging Generative AI
Helping translate investment frameworks from various segments of the market (i.e. cash flow driven investing, liability driven investing, insurance capital efficient portfolio construction) into tangible investment and client engagement tools
Leveraging advanced optimization techniques to create optimal portfolio solutions for internal and external stakeholders
Employing multi-period simulation tools to project and optimize performance in the context of flows and optionality
Integrating third party risk models in various portfolio construction exercises
Designing and maintaining procedures and tools that make data management and research more efficient
Working closely with other quantitative and technology teams in the firm in leveraging best practices from a financial theory and technological perspective.
Formulating new ideas for research that will help enhance frameworks and tools
Following academic research and industry trends to constantly incorporate best practice

Advanced degree in quantitative disciplines such as engineering, finance, operations research, or computer science is required
Solid understanding of standard financial engineering techniques
Excellent knowledge of statistics and optimization
Strong Python programming expertise, including experience with FastAPI for building RESTful services, Numba for high-performance computing, and deploying solutions on cloud environments (AWS, Azure, or GCP)
Strong background in technical infrastructure development, Git, and cloud technologies is advantageous
Researching, implementing, and integrating Large Language Models (LLMs) and conversational AI technologies to develop intelligent client engagement tools and chatbots will be beneficial
Experience managing and manipulating large data sets (preferably in SQL)
Strong ability to learn and translate abstract principles into systematic algorithmic representations
Some experience using third party risk models such as BarraOne or Axioma will be a plus
Progress towards CFA designation preferred.

Flexible paid time off
Hybrid work schedule
401(K) matching of 100% up to the first 6% with a discretionary supplemental contribution
Health & wellbeing benefits
Parental Leave benefits
Employee stock purchase plan

Quantitative Analyst, Portfolio Engineering – Analytics

Key skills