Source description
About the role
Design, build, and deploy production-grade machine learning models and analytical solutions that are robust, scalable, and maintainable by other data scientists.
Apply a broad range of statistical methods and ML algorithms, using sound judgment on when — and when not — to use them.
Write highly performant, well-documented, and reproducible code in Python and SQL.
Collaborate with product managers, business stakeholders, and engineering teams to clarify requirements, scope analytical work, and deliver on project milestones.
Make thoughtful trade-offs between model performance and interpretability, complexity and simplicity, and computational cost and accuracy.
Identify and resolve data quality issues and analytical gaps; drive improvements to data science workflows and model reliability.
Actively contribute to model reviews, experimental design discussions, team planning, and post-deployment performance evaluations.
Work to find and address root causes of model performance issues, leaving systems better than you found them.
Contribute to on-call support and take ownership of issues, driving resolutions or ensuring clear handoffs.
Automate manual reporting tasks and contribute to operational excellence across the team.
Mentor junior data scientists and actively participate in the hiring and interview process.