Senior data scientist San Francisco

About Watershed

Watershed is the enterprise sustainability platform. Companies like Airbnb, Carlyle Group, Fed Ex, Visa, and Dr. Martens use Watershed to manage climate and ESG data, produce audit-ready metrics for voluntary and regulatory reporting including CSRD, and drive real decarbonization. We are looking for team members who love product-building, want to work hard at a mission-oriented startup, and will collaborate with us in shaping the culture of a growing team.

We have offices in San Francisco, New York, London, Paris, Berlin, Sydney, Mexico City, and remote team members across the US and Europe. We hope that you’ll be interested in joining us!

We are seeking a highly skilled and analytically-minded Senior Data Scientist to join our team. In this role, you will collaborate with climate scientists, data engineers, and product managers to design and build the statistical foundations of our core datasets—including CEDA, our industry-leading Scope 3 emissions dataset, and Cornerstone, our open data initiative. You will focus on the analytical and methodological challenges of creating reliable, defensible sustainability data that companies can trust for high-stakes decisions.

As part of this work, you will:

• Design and validate the statistical methodologies underlying our emissions datasets, ensuring they are scientifically sound, transparent, and fit for purpose.
• Build data processing pipelines that incorporate statistical rigor—handling missing data, quantifying uncertainty, and implementing quality control measures that catch anomalies and errors.
• Develop frameworks for evaluating data quality, coverage, and reliability across heterogeneous sources, and communicate these trade-offs to technical and non-technical stakeholders.
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Partner with climate scientists to translate research insights into production data systems, bridging the gap between scientific methodology and scalable engineering.
• Analyze and model sustainability data to uncover patterns, validate assumptions, and surface insights that improve our datasets and inform product development.
• Contribute to open data initiatives and peer-reviewed academic publications, ensuring our methodologies are well-documented, reproducible, and advance industry standards.
• Mentor other data scientists and engineers on statistical best practices and analytical thinking.

You might be a fit if you:

• Have 5+ years of industry experience in data science, applied statistics, or a related quantitative field, with a track record of building data products or analytical systems.
• Have strong foundations in statistics and quantitative reasoning—comfortable with concepts like uncertainty quantification, bias‑variance tradeoffs, experimental design, and statistical modeling.
• Are fluent in Python and SQL, with experience building data pipelines that incorporate analytical logic and statistical validation. Experience with data transformation frameworks is a plus.
• Have experience working with messy, real‑world data and making principled decisions about how to model, clean, and harmonize it into reliable datasets.
• Care about both analytical rigor and engineering craft: you write well‑tested, maintainable code and design solutions that are scientifically defensible and operationally robust.
• Communicate complex analytical concepts clearly to diverse audiences, from climate scientists to product managers to customers.
• Thrive in environments that require both systematic thinking and attention to detail—balancing methodological soundness with the need to move quickly.
• Are motivated by sustainability challenges and excited to work at the intersection of data science, climate science, and impact.

Must be willing to work from an office 4 days per week (except for remote roles)

Watershed has hub offices in San Francisco, New York, London and Mexico City, and satellite offices in Sydney, Paris, Berlin. Where we have offices, employees are expected to be in office for 4 days per week. Certain jobs are open to being remote and will be specifically noted on the jobs page and in the job description if so.

What’s the interview process like?

It starts the same for every candidate: getting…