Research Analyst

We're building the world model for a resilient global economy.

The global economy runs on physical assets: fabs, plants, mines, warehouses, data centers, pipelines. A drought, a port closure or a failed supplier moves through those assets into company earnings, and from there into markets. Our mission is to make the global economy more resilient by translating the physical world into financial impact.

We start with asset-level intelligence for financial markets. Kepler maps the physical assets of every company we cover, models what each one produces and earns, and connects them to the company's financials and to each other — who supplies whom, where revenue concentrates, and what can be substituted.

On that foundation, we layer risk. Physical risk is the first layer, and it's live today: drought, heat, flooding and wildfire, turned into asset-level, dollar-denominated, point-in-time Earnings-at-Risk built to sit next to a Bloomberg feed on an investor's desk. Supply-chain dependencies come next, then geopolitical concentration, with more layers to follow.

Our data science team builds the models and turns them into research: a monthly El Niño update on which sectors and regions are exposed, and deeper analysis for customers who ask for it. Two data scientists do all of that today, alongside building the next peril.

This role is the second analyst on that team, a step more junior. You'll learn the use cases from the data scientists who built the models.

The monthly research, with our forecasting lead. When a new forecast lands, you help produce the El Niño update: the free public version and the deeper version for subscribers.

Bespoke analysis, with our data science team. A customer asks for a look at a company, a sector or their portfolio, and you do it. You’ll comb through the climate and financial data to find interesting signals and threads to explore.

A data analyst, ideally from an asset-management context. If not, a strong analyst who picks up the customer problem fast.

Comfortable working directly in our data.

2–5 years. You don't need to have worked on physical risk before.

Someone our data scientists would have hired onto their own team in finance.

And you're someone we'll want to build alongside for thousands of hours.

• Exposure to financial markets or physical-risk data.

• Geospatial work: maps, rasters.

• Published research or writing you can show us.

We'd rather hire someone very smart and very curious who can grow into the domain than someone who ticks every box.

• $120K–$150K + 0.15–0.35% equity.

• Health-insurance reimbursement (QSEHRA) and a real commitment to grow benefits at every raise and milestone.

• An awesome office space to work from in San Francisco.

Curiosity. Pragmatism. Empathy. Transparency. Care.

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 — your background, role by role

4. Working Session + Success Plan · 120 min (2 parts) — take a slice of our data, do the analysis, and produce one published figure that answers a question an asset manager would ask; present it, then discuss how you'd succeed in the role (adapted from DS P&V: interactive-view half dropped)

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]