Staff Applied Data Scientist, Steadily

• Experienced: 5+ years experience applying Data Science methods to production problems. We expect you to be able to dive into a complex codebase without too much spin-up. Past experience as a team lead is definitely a plus
• Builder: You like the product-side of data and think about how to apply modeling and evaluation techniques to real-world problems. You have thoughtful opinions about where the data leads and how to maximize the business impact of your work
• Pragmatic: We prioritize impact and delivery. You balance speed and quality, making thoughtful trade-offs to solve problems effectively. You leverage off-the-shelf solutions so we don’t re-invent the wheel but understand when a custom solution is appropriate
• Curious: you are not just an order-taker... you are curious about what makes the business tick and you learn the intricacies of how it runs. This results in strong intuition for when analysis is wrong and leads you to suggest ideas and insights that nobody thought to ask for. You’re not the type of scientist who wants fully fleshed-out specs thrown over the wall for you to implement

Desirable

• You will play a key technical role on our Engineering team, identifying and evaluating trends, insights across large data sets and where having more refined data or internal ML/AI models could improve our product outcomes or operations
• You’ll own building, evaluating, and deploying these models to production and monitor for quality and accuracy over time to prevent regressions and ensure continued relevance
• We operate across data types including public, proprietary and a large volume of image data
• You’ll operate with a high degree of autonomy and serve as a trusted technical owner for business problems across the organization
• Steadily is still early in our exploration and application of where AI/ML models can drive the biggest business value, so this role is ideal for Scientists who like operating in ambiguous environments and to explore where the most impactful focus and methods should be applied
• Design, build and evolve data sets and models with an emphasis on scalability, quality and maintainability, identifying the appropriate technique and approach to meet the needs of the business. Focus areas could be estimating risk at the property level, how to accurately assess property costs and using aerial image analysis or modelling techniques to identify individual attributes that feed into other models
• Own and lead exploration and implementation of new areas of science application in our product ecosystem to better predict risk both on a per-insured level and in aggregate across the entire portfolio
• Write clean, maintainable R/Python code, setting a high bar for quality and adherence to best practices
• Partner closely with Engineering, Product, Operations and Business teams to design reliable solutions across systems
• Provide excellent metrics and visibility into model quality, bias and performance to assess how it’s helping the business ensure a high bar of scientific rigor and evaluation