Senior Quantitative Researcher

Numerai; San Francisco (CA); On-site; USD 200,000 - 300,000; Full time

Job summary

Numerai is hiring a senior quantitative researcher to expand the universe of signals and build robust risk models. You will develop alpha across horizons, work on covariance, factor risk estimation, and portfolio optimization, and ensure research becomes production-grade.

The role emphasizes deep statistical judgment, experience with real capital strategies, and strong communication to persuade stakeholders. Expect ownership of end-to-end Python workflows, from research to deployment.

Qualifications

• PhD or equivalent depth in quantitative field.
• 7+ years in quantitative research with real capital experience.
• Deep knowledge of factor models, risk decomposition, portfolio optimization, and execution.
• Strong statistical judgment to avoid overfitting and misleading results.
• Excellent written communication; reports should influence decision makers.
• Comfortable owning end-to-end work in Python, including non-glamorous parts.

Responsibilities

• Expand the set of signals Numerai knows to pursue and build robust risk structures.
• Develop and evaluate alpha across horizons and data types for production use.
• Build and refine covariance, factor risk estimation, and optimizer design with robust diagnostics.
• Produce research reports and productionize successful ideas for live deployment.

Skills

Factor models

Risk decomposition

Portfolio optimization

Transaction cost modeling

Execution

Education

PhD in mathematics, statistics, physics, EECS

Tools

Python

Job description

Numerai is building a self-improving hedge fund.

We run an institutional quant fund built from thousands of machine learning models. Increasingly, those models are built by AI, not people. This isn't a thesis we're pitching. It's already how Numerai gets smarter every day (see https://numer.ai).

The next step is to compound it. Numerai is forming a new team with one mandate: build AI that does quant research autonomously, all the time. Discover new features, build better risk models, improve how the fund turns thousands of signals into a portfolio.

Numerai is a small company with an unusual amount of leverage per person. No bureaucracy, no politics. Significant equity.

The Role

Your job is to expand the set of things Numerai knows how to do: which signals are worth pursuing, which risk structures are worth taking, which ideas look good in backtest and reliably die in production, and where the market is exploitable.

You will develop and evaluate alpha across horizons and data types, from slow fundamental structure to faster cross-sectional effects.

You will work on covariance and factor risk estimation: estimator choice, shrinkage, conditioning, missing and stale data, and the difference between a number that looks stable and a number that is actually right out of sample. You will build the diagnostics that catch a degrading risk model before the portfolio does.

You will work on the optimizer: problem formulation, constraint design, objective specification, numerical conditioning, solver behavior, and the sensitivity of the resulting portfolio to every assumption baked into it. You should be the person who notices that a constraint is quietly doing something nobody intended.

You will help decide what data we buy and what we build, and you'll productionize what works.

You will write up what you find. Research reports, derivations, and clear explanations of why the new formulation is better than the old one. Then you will ship it to production and monitor it.

Requirements

• PhD in mathematics, statistics, physics, EECS, or a comparably quantitative field — or equivalent demonstrated depth
• 7+ years in quantitative research, ideally across more than one firm, with strategies that traded real capital and made money
• Deep working knowledge of factor models, risk decomposition, portfolio optimization, transaction cost modeling, and execution
• Strong statistical judgment: you have internalized how overfitting actually happens and you design research to avoid fooling yourself
• Excellent written communication. Your research reports should be able to change someone's mind
• Comfortable owning work end to end in Python, including the unglamorous parts

Nice to have

• Global equities and cross-sectional stock selection experience
• Familiarity with modern portfolio risk and construction literature
• Experience with numerical linear algebra at scale, or with conic and quadratic solvers
• Experience with machine learning applied to financial prediction, and a clear sense of where it helps and where it doesn't