Quantitative Researcher

We created Frec to expand the possibilities for everyone and their money. We’re a tight team of builders, creators, and designers who want to be smarter with our money for our futures and our families. As a quantitative developer at Frec, you’ll create products that enable us to level the financial playing field and empower people to manage their own money. Some values we identify with are accountability, humility, compassion, and teamwork. If this resonates with you, join us at this pivotal time to help shape our structures, systems, and Frec’s future. Frec highly values product ideas and feedback from all employees, in a true bottoms-up fashion. This means as a quantitative developer, you will work with a growing team comprised of quantitative researchers, software engineers, product managers, designers, and brokerage operations professionals to ideate, prioritize, prototype, develop, test, and iterate on software that will educate and equip people with newer and smarter ways to build wealth. We’re looking for engineers who have an unrelenting sense of urgency and capability to move fast, have a strong sense of ownership, ability to deal with ambiguity and are enthusiastic about tackling new challenges to help us build a world-class financial platform. 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 GraphQL as the mode of building and exposing APIs Contact If all of the above resonates with you, reach out to us at View email address on click.appcast.io and join us for the ride! #J-18808-Ljbffr