Advanced degree in a quantitative field (e.g., Engineering, Computer Science, Applied Mathematics, Physics).
Strong analytical mindset with a curiosity for investment management.
Knowledge of investment/finance concepts, particularly portfolio theory and tax-aware investing.
Proficient in object-oriented programming languages.
Excellent problem-solving skills and attention to detail.
Ability to work independently and collaborate effectively in a team environment.
Responsibilities
Own the design and refinement of core methodologies for long-only and long-short direct indexing engines.
Shape portfolio construction, rebalancing, and performance attribution methodologies.
Analyze large financial datasets and design backtesting frameworks.
Conduct factor and attribution analyses, ensuring model validity under dynamic market conditions.
Collaborate with cross-functional teams to integrate research outputs into trading systems.
Benefits
Competitive salary and equity grants.
Fully paid health, vision, and dental insurance.
401k retirement plan.
Monthly health and wellness allowance.
Unlimited paid time off for flexibility.
Visa sponsorship and immigration support.
Complimentary daily lunch and dinner at the office.
Full Job Description
What you will do:
Quantitative Research & Strategy Development: You will live at the intersection of mathematics, finance, and statistics. You will own the design, validation, and refinement of the core methodologies driving our long only and long-short direct indexing engines, including tracking error minimization, tax-loss harvesting, and risk-aware portfolio construction. You take pride in translating ambiguous investment questions into rigorous, well-tested models with clear empirical grounding.
Portfolio Optimization & Performance Research: You will help shape our portfolio construction, rebalancing, and performance attribution methodologies. You care deeply about the trade-offs between tracking error, tax efficiency, and transaction costs, continuously researching improvements to our optimization formulations and ensuring that every methodological decision is robust under varied market regimes and supported by quantitative evidence.
Financial Data Analysis & Modeling: You'll tackle complex challenges around analyzing large financial datasets, including market data, execution data, tax lots, corporate actions, and commercial risk models. This includes designing backtesting frameworks, conducting factor and attribution analyses, and ensuring our models behave correctly under dynamic market conditions before they reach production.
Collaboration: You'll partner closely with quantitative developers, backend engineers, as well as product, design, and operations teams, to ensure that research outputs translate into systematic trading systems that are mathematically accurate, technically sound, operationally robust, and seamlessly integrated into high-quality product experiences.
What we offer:
Competitive salary and equity grants
Fully paid health, vision and dental insurances
401k
Monthly allowance to help with maintaining a healthy body and mind (fitness & mental health components)
Flexible (Unlimited) paid time off
Visa sponsorship & immigration support
Daily in-office lunch and dinner
Office in San Francisco/New York for in-person collaboration (close to public transit options)
Requirements:
Advanced degree in a quantitative field such as Engineering, Computer Science, Applied Mathematics, Physics.
Strong analytical mindset with intellectual curiosity in investment management
Investment/finance knowledge, including portfolio theory, factor models, and tax-aware investing (experience with Cash Equities is a plus)
Strong problem solving skills and attention to details, and ability to explain the ideas that underlie them
Strong programming background in an object oriented language.
A self-starter who embraces ownership and accountability, should have the ability to work independently as well as thrive in a team environment
Tech Stack:
Python as the primary research environment, with TypeScript/Node powering production systems
PostgreSQL as our data store, with Redis for caching and distributed coordination
Commercial risk models (e.g. Barra) and convex optimization tooling for portfolio construction
dbt and notebook-based analytics over a dedicated analytics replica
Deployed on AWS using containerized infrastructure
