Senior Data Scientist — Solve Team

Remote, Eastern Europe or LatAm · Contract (B2B)

About the Company

We are a quantitative investment firm based in San Francisco. The firm automates investing decisions based on business fundamentals, combining data science, machine learning, and disciplined engineering practice, and runs a market-neutral long/short equity strategy driven by its own machine-learning platform.

The team is small and distributed across North America and Eastern Europe. Two of the firm's stated principles shape the research work directly: reason from first principles, and communicate openly and precisely.

Role Overview

Our Predict team decides what the firm thinks about each company. The Solve team decides what the firm actually does about it.

You take model output across a universe of roughly 2,000 US-listed companies and turn it into a portfolio that can be traded: sizing positions, respecting risk and exposure constraints, accounting for transaction costs, and deciding how much of the portfolio to move at each rebalance. You then work on making those decisions measurably better.

This is an optimization and quantitative research role. The question is not "what will this company do" — it's "given what we believe, what's the best portfolio we can hold, and what is it costing us to get there."

The Problem, In Concrete Terms

• Strategy: Market-neutral long/short equity on US-listed companies, driven by fundamental data. Longer holding periods and smaller companies than most quantitative investors, and explicitly not high-frequency trading.
• Universe: Roughly 2,000 US-listed companies.
• Cadence: Medium frequency — the portfolio is rebalanced on a daily-to-weekly cadence.
• The work: Portfolio construction and optimization, risk and factor exposure, transaction cost and turnover, and improving the efficiency of the strategy end-to-end.
• Stack: Python and AWS.

Why Join

You'll work directly with the people who set investment priorities, alongside a small, capable team of data scientists, engineers, and portfolio managers. The Predict team's output is your input — you sit at the point where signal becomes an actual, tradeable portfolio, and your work has a direct, measurable line to what the firm holds.

Candidate Profile

The ideal candidate has real hands-on experience in portfolio construction and optimization — not just familiarity with the theory, but time spent making sizing, risk, and turnover trade-offs on a live book. You're comfortable with convex optimization tooling, understand risk decomposition and PnL attribution, and can work through the full research cycle yourself, from formulating the optimization problem to defending the result to the team.

Key Responsibilities

• Apply data science and machine learning tools to combine alpha signals into a single return forecast.
• Work on portfolio optimization projects related to volatility estimation, risk control, and related areas.
• Work closely with a small, capable team of data scientists and engineers.
• Maintain research code and ensure results used in reports are reproducible by others on the team.

Tech Stack

• Python, Jupyter, NumPy, Pandas, SciPy
• Convex optimization tooling: cvxpy, cvxopt, or equivalent
• SQL
• AWS, in particular S3 and Athena
• Git and GitHub — pull requests, code reviews, unit and integration tests, continuous integration
• POSIX-like environments: Linux or macOS, bash, ssh

Requirements

• Education in mathematics, statistics, computer science, or a similar field (PhD is a plus but not required).
• 4+ years of professional hands-on quant finance and data science trading experience.
• Understanding of risk models, PnL attribution, and risk decomposition.
• Mastery of Python and modern data science software tools and practices.
• Hands-on familiarity with common software engineering practices and tools (git and GitHub, pull requests and code reviews, unit and integration tests, continuous integration).
• Working knowledge of POSIX-like environments (Linux/macOS, bash, ssh) and AWS (Athena, S3, SageMaker, Step Functions) is strongly preferred.
• Excellent verbal and written communication in English (Russian is a plus).

Nice to Have

• Experience at a quant fund, proprietary trading firm, or systematic investment manager.
• Equity market-neutral or long/short experience specifically.
• Understanding of fundamental equity analysis and the data that supports it.
• Experience working with a small book where implementation cost genuinely constrains the strategy.

Terms

• Remote, from Eastern Europe (preferred) or LatAm.
• Daily overlap with the US team from 6:00–9:00 AM Pacific Time (15:00–18:00 CEST / 16:00–19:00 EEST).
• Compensation from USD 8,000/month.
• Performance bonus of 25–50%, based on firm performance and individual performance.
• B2B contract, paid directly with a US legal entity or through Deel.
• Daily stand-ups and a weekly review, where you present progress and findings.
• The firm is not able to contract with candidates residing in Russia.

Hiring Process

1. CV review
2. Intro call with Recrucial
3. Technical screening call with a quantitative researcher from the firm
4. Test problem on real data
5. Interview on the test problem
6. Final interview with firm leadership (up to three interviews)