Climate Data Scientist

Role overview

• check_circle Carrying out literature reviews of existing methodologies and datasets
• check_circle Identifying gaps in existing 1-to-10 year prediction products
• check_circle Developing multi-method approaches that result in unique Climate Central product offerings
• check_circle Using climate models and data to develop and validate predictions of natural hazards or perils in the 1-to-10 year window
• check_circle Developing scientifically sound technical reports that clearly convey data uncertainties and methodological details to build trust in forecast products developed for end users
• check_circle Presenting results at scientific conferences
• check_circle Developing statistical models that predict local climate risks and collaborating with technical staff to turn these into operational service products
• check_circle Working with our technical, engagement, and business development teams to create forecast products that meet end-user needs
• check_circle Working with our communications team to ensure products are accurately and accessibly reflected in externally facing materials
• check_circle This position requires an advanced degree in a relevant field and experience applying data-driven statistical approaches to climate model outputs
• check_circle Experience with rigorous model evaluation, uncertainty, and validation techniques for probabilistic statistical modeling
• check_circle Experience analyzing and visualizing large climate and geospatial datasets in Python using packages such as Xarray, pandas, statsmodels, and scikit-learn
• check_circle Ability to work collaboratively within a fast-paced environment and to see perspectives across different disciplines and vocabularies
• check_circle Excellent communications skills with a track record of effectively communicating about data and scientific findings

Preferred qualifications

• Experience working for a federal agency or lab that has been impacted by recent cuts
• Comfort in working in GitHub and with parallel and distributed computing tools to turn complex big data projects into efficient analysis pipelines (e.g., Dask, Zarr, multiprocessing, joblib, and high-performance computing or cloud workflows) and end-user-ready products
• Experience working with seasonal-to-decadal forecasting models and/or large ensembles
• Familiarity with novel AI and/or supervised and unsupervised machine learning techniques, including using Python packages such as PyTorch, TensorFlow/Keras, or XGBoost
• Familiarity with the science and/or data relevant to quantifying climate risk in one or more sectors, such as health or economics
• Experience in working with statistical and/or dynamical downscaling techniques and bias-adjustment for producing fine-scale resolution information
• Experience producing scientific manuscripts and reports
• Medical benefits
• Dental/ Vision benefits
• More than 7 weeks of paid vacation and holidays available annually
• 12 weeks of paid family leave
• 401k with up to 12% matching
• Partial college tuition for children of employees who qualify

About the company