Founding Data Scientist

We're building the missing data layer in financial markets: physical risk.

Physical risk — drought, heatwaves, flooding, wildfire — impacts more than half of global GDP and costs companies hundreds of billions of dollars per year.

But markets can't price it. The data that exists is vague: climate "scores" and 2050 scenarios no investor can actually rely on.

We're already working with 3 of the world's top 10 asset managers (over $30 trillion AUM) to solve this problem.

Kepler turns events in the physical world into a number investors trust — asset-level, dollar-denominated, point-in-time Earnings-at-Risk, built to sit next to a Bloomberg feed on an investor's desk.

For physical risk to matter to markets, someone has to prove it's real — not a plausible-sounding score, but a signal that actually predicts what happens to a company's earnings, holds up out-of-sample, and survives a quant team's scrutiny. That proof is the difference between Kepler being interesting and Kepler being indispensable.

Closing that gap, and turning our working system into the category-defining global risk platform, is the central engineering challenge of the company.

Our 10-year vision: Kepler is one of the most important companies in finance, thanks to a world model that can accurately predict how events in the physical world will impact assets, companies, and markets.

You'll be our first data scientist, and you'll own the evidence that helps us build toward this vision: whether the signal is right, where it breaks, and how good it can get.

• The proof — backtests that test whether our signal actually predicts financial outcomes, across perils, sectors, and market regimes, and that stand up when a buy-side quant team pushes back.

• The methodology — how physical hazard at a facility becomes revenue and earnings impact: the assumptions, the attribution, the point-in-time discipline.

• The product's direction — what you learn about where the signal is strong and weak becomes the roadmap for what we build next.

The research agenda here is wide open. You'll help set it.

• A markets-or-research brain: you think in hypotheses, controls, and falsification, and you're comfortable being wrong on the way to being right.

• A strong quantitative foundation — backtesting, statistical modeling, hypothesis testing, signal analysis.

• Fluent in Python + SQL; you can take an analysis end-to-end without waiting on an engineer.

• Experience in financial markets — any one of:

  • 3–5 years in a quantitative/research analytics role, or

  • a Master's + 3 years, or

  • a relevant PhD + 2 years.

• Backgrounds that fit (non-exhaustive): quant or systematic investing, risk modeling, econometrics, data science in finance or insurance, or academic research in a quantitative field (fun fact: some of the best quants hold a PhD in astrophysics!)

• Judgment to optimize for speed and impact over endless precision — and the taste to know when precision actually matters.

• Exposure to financial markets or the buy-side — or a genuine obsession with how markets price things.

• Geospatial / remote-sensing or climate-risk data experience.

• Comfort walking a customer through your methodology.

• $150K–$190K base + 0.5–1.0% equity.

• Health-insurance reimbursement (QSEHRA), 2 weeks PTO to start, growing with the company.

• An awesome office to work from in San Francisco

• Direct influence on the direction of the product and company as a founding team member

Curiosity. Pragmatism. Transparency.

1. Intro call with CEO · 15 min — mutual fit

2. Intro call with CTO · 30 min — your technical background

3. Deep dive with the founders · 60 min — more about your background

4. Working Session + Success Plan — 120 min (2 parts) — present your work on an assigned problem; discussion re: how you can succeed in the role

5. References

6. 1-Day Working Interview — assessing long-term fit together

7. Offer

Fill in the form here and upload your CV. Questions? Email [email protected]