What you'll do:

• - Own a domain such as credit, risk, or growth end to end, setting the modeling strategy and being accountable for the systems that result.
  - Take on the most ambiguous, highest-stakes problems, including framing what should be built and why before any modeling begins.
  - Make and defend the hard technical calls, choosing the simplest approach that solves the problem rather than the most sophisticated one.
  - Drive models into production and own their long-term health, performance, and reliability once they're live.
  - Set technical direction and standards that other data scientists build on, through design reviews, shared tooling, and engineering practices.
  - Mentor data scientists across levels and raise the quality bar for the team's work.
  - Translate business strategy into a coherent data science roadmap and communicate tradeoffs clearly to leadership.
  

To succeed in this role, you'll need to have:

• - 5+ years of hands-on data science or applied ML experience, with a track record of owning systems in production end to end.
  - Deep proficiency in Python (NumPy, pandas, scikit-learn) and SQL, and strong software engineering fundamentals.
  - Strong grounding in statistics, probability, and machine learning, with the judgment to know which method actually fits the problem.
  - Experience designing and operating large-scale ML systems on cloud platforms (AWS preferred).
  - A demonstrated ability to lead ambiguous, cross-functional initiatives without close guidance.
  - Excellent communication skills, including the ability to influence technical and business stakeholders and to make others better through your feedback.