The role

As a Senior Data Scientist focused on Climate Risk, you will contribute to the evolution of UrbanFootprint’s climate and hazard risk-based data products, including Physical Climate Risk, Grid Resilience Insights, and Municipal Bonds Insights.  These products are designed to help asset managers and financial institutions make investment decisions by taking into account climate hazard risk, community impacts and vulnerability, and physical infrastructure and the built environment.  These data products leverage our parcel-level map of all properties across the US to facilitate decision-making for scales ranging from parcels and neighborhoods to states and the entire US.

As a product-minded and pragmatic data scientist, you know how to appropriately scope projects to ensure we deliver what customers need today and have ideas for improving in the future.  You are autonomous, not independent;  you work collaboratively with business partners to understand the ‘what’ and ‘why’ and take full ownership of figuring out the ‘how’ to meet those needs.  You proactively communicate progress and are accountable for the validity, accuracy, and applicability of all the models you build.  You take pride in the models you build and recognize that solutions don’t need to be perfect to make a difference in the world.   

What you’ll do:

• Propose and develop novel models and algorithms to quantify climate impact that reflect risks to business and society.
• Own data science problems end-to-end, from ideation, exploratory data analysis, and prototype to collaborating with data engineers to ship models to production.
• Identify, normalize, and analyze various historical weather and climate datasets to assess where risks are the highest.
• Help to define and communicate a quantified understanding of risk across multiple products.
• Communicate technical information, such as model methodology, accuracy, and weaknesses, to non-technical partners regarding product, sales, and customer success.

Your background most likely includes:

• Work experience equivalent to a Master’s degree or higher in Earth system sciences (Earth, Atmospheric, Oceanic,  or Geo Sciences), Hazard, Disaster, or Catastrophe Science, Statistics or Computer Science (with an emphasis on weather or climate modeling), or similar technical fields with an emphasis on geospatial, spatiotemporal or machine learning modeling.
• Experience working with publicly available climate and historical weather data, including CMIP6 and downscaled CMIP6 datasets (e.g., LOCA), reanalysis data (e.g., MERRA, ERA-5, NARR), and others.
• Practical experience with various statistical and machine learning models applied to climate and weather modeling, such as climate model downscaling, prediction of extreme events, or flooding modeling. 
• Experience estimating risk or quantifying the impact of climate or weather-related events (e.g., damage estimates, rebuilding costs, population impacts, or economic losses).
• Fluency in Python and Python’s scientific programming stack, such as pandas, GeoPandas, rasterio, pangeo, sklearn, pytorch, fastai, statsmodels, and various visualization packages, including map-based visualizations.
• Experience developing models in an iterative, fast-paced environment.
• Excellent communication, collaboration, and documentation skills.

Bonus qualifications:

• Experience with Catastrophe modeling or process-based models of flood, wildfire, or hurricanes.  
• Experience working with satellite data such as LandSat or MODIS.
• Experience developing and scaling models in a cloud environment (GCP preferred).
• Deep experience with geospatial modeling and analysis, including familiarity with raster rescaling, joining rasters to vector features, and spatiotemporal modeling.
• Experience as a full-stack data scientist, owning data pipelines, model development, and production model implementation.
• Expertise in large-scale data analysis frameworks, including SQL/PostgreSQL, Apache Beam, Dask or PySpark.
• Passion for climate resilience, equity, urban planning, and leveraging data to facilitate a more equitable and resilient society.

Target salary: $130,000 - $200,000, plus bonus eligibility and equity.

(Compensation is flexible based on experience; all candidates at various levels will be considered.)