Sr. Data Scientist

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  Lead acquisition, processing, and analysis of climate-related and geospatial data for First Street modelers and data partners.

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  Develop and maintain scalable data pipelines on local and cloud-based systems (AWS preferred).

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  Perform statistical analysis to validate hazard model predictions and assess model uncertainties.

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  Develop and enhance machine learning models supporting First Street's peril product suite.

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  Plan, execute, and direct Unix-based workflows using Python, Bash, GDAL, and related technologies.

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  Analyze raster and vector data at scale to improve model accuracy, identify quality control issues, and develop remedies.

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  Design and implement quality assurance checks on climate model data and derived statistics.

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  Contribute to the broader Data Science and Data Engineering team's success through technical leadership and mentorship.

• Master's Degree + 4 years of experience, or Ph.D. + 2 years of experience (field: Data Science, Earth Science, or related)

• Python — required

• Linux & Bash — required

• Git & source control — required

• Cloud computing — required (AWS strongly preferred)

• Geospatial data and formats: GeoTIFF, NetCDF, GRIB, HDF5, Zarr

• Strong understanding of probability and statistics as applied to spatial data

• Experience with big data analysis, parallel processing, and batch/spot workflows

• Machine learning model development — ideally a model you built yourself, not just contributed to as part of a team

• High-performance computing experience (SLURM is a plus)

• Agentic AI applications, with an understanding of the practical benefits and limitations of AI agents

• Experience mentoring junior scientists or engineers

• Background in the physical sciences
  

Our anticipated US base salary compensation range for this role is $100,000-150,000 plus stock options and a competitive benefits package, which includes equity, 401(k), paid time off, paid parental leave, and comprehensive health benefits. Actual compensation will vary depending on factors such as work location as well as additional factors such as a candidate’s qualifications, skills, experience, competencies, and relevant education. Your recruiter can share more about the specific salary range for your location during the hiring process.

• Impact: We only focus on things that move the needle

• Drive: We are driven by the role we play in connecting climate change to financial risk

• Ownership: This is our company and we act accordingly

• Urgency: We move quickly because the world depends on it

• Resilience: We have a growth mindset in all that we do