Position SummaryWe are seeking a Quantitative Analyst with three or more years of experience to help advance our research and investment analytics capabilities, with a particular emphasis on data science. This highly capable person will build and maintain a thoughtful data layer across portfolios, managers, and asset classes, while developing analytical tools and workflows that improve decision-making and efficiency. This is a hybrid role that blends investment domain knowledge with data science and applied AI, offering an opportunity to help shape the evolution of our investment platform.
This role is located in Boston, MA.
Primary Responsibilities- Partner with senior investment professionals and other colleagues to translate strategic priorities into practical analytical solutions, workflow enhancements, and automation opportunities.
- Support the Investment team in monitoring existing investments and underwriting new opportunities, including internal discussions related to portfolio construction, performance, and risk.
- Build and maintain scalable data pipelines and workflows to support reliable data ingestion, transformation, and quality across investment, portfolio, and client datasets.
- Help develop and enhance the firm's investment data layer, with a focus on making information more structured, accessible, and actionable across asset classes and investment processes.
- Analyze large and complex datasets to identify trends, behavioral patterns, performance drivers, and other insights that can inform investment decision-making.
- Design, validate, and operationalize analytical models and AI-enabled tools to support areas such as asset allocation, manager research, portfolio monitoring, and risk assessment.
- Maintain strong documentation and adhere to appropriate data governance, control, and compliance standards consistent with industry best practices.
QualificationsRequired
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Finance, or a quantitative field.
- Three or more years of relevant experience in data science, analytics, investment research, or a related role, ideally within financial services, asset management, or wealth management.
- Experience using Git and GitHub for version control, collaborative development, code reviews, and managing production-grade analytical or quantitative applications.
- Strong programming skills with experience building analytical workflows, models, and data pipelines.
- Experience working with modern data infrastructure, including Snowflake or similar environments.
- Solid understanding of financial markets, portfolio construction, investment performance, and risk concepts.
- Excellent communication skills and the ability to explain complex analyses and technical concepts clearly.
Preferred
- Experience with business intelligence and data visualization platforms such as Power BI.
- Familiarity with cloud environments such as AWS, Azure, or GCP.
- Experience applying machine learning or AI tools in production or business workflows.
- Experience with agentic workflows using tools such as Copilot Studio or Claude.
- Familiarity with modern LLM ecosystems and related concepts such as retrieval-augmented generation (RAG), vector databases, agent frameworks, or tool/function calling.
- Exposure to investment manager research, underwriting, or portfolio analytics is a plus.
This position is an exempt position. The annualized base pay range for this role is expected to be between $150,000-$250,000 base salary compensation range. Actual base pay may vary based on factors including, but not limited to, experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set. The base pay is just one component of the total compensation package. Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life insurance, and 401(k). Please note that the job title is subject to change based on the selected candidate's experience and education.
#LI-SF1
