Note: The job is a remote job and is open to candidates in USA. Wisely Optimize is a basketball analytics company focused on transforming complex data into actionable insights for coaches and decision-makers. They are seeking a Basketball Data Scientist to lead modeling work related to player evaluation and projections, ensuring that analytical outputs are effectively communicated and utilized by staff. This role is equity-only and designed to fit around a full-time job, emphasizing outcomes over hours worked.
Responsibilities
- Lead modeling work day to day — the player evaluation, projections, and fit analysis that coaches use to make roster decisions
- Get modeling work into production where it actually reaches users
- Build models from scratch and pressure-test, sharpen, and productionalize inherited models
- Perform feature engineering, validation, and determine when a simple explainable model is preferable to a complex one
- Translate model outputs into understandable metrics for staff to act on
- Maintain clean code and reproducible pipelines for models
- Conduct data engineering to ensure models are fed with fresh data
- Communicate clearly about progress and blockers during daily standups
Skills
- Proven, elite analytical work. This is the one area we need you strong in from day one — it's not learn-on-the-job. You're excellent with statistical modeling, validation, and knowing what a number actually means
- Strong Python and SQL, and comfortable working directly with data rather than waiting for someone to hand you a clean table
- Comfortable with engineering, not just notebooks: you can write code others can run, get a model into production, and do the data engineering work it takes to keep it running
- Deep knowledge of advanced basketball analytics — you know the modern metrics, their tradeoffs, and can argue about how to measure a player with numbers
- Coachable: happy to inherit someone else's model, take feedback, and get coached on the basketball side. What ships matters more than whose idea it was
- Discernment over complexity: you'd rather ship a model a coach trusts than a model that scores well and never gets used
- High motor and follow-through: you communicate clearly and ship consistently
- A passion for basketball and a desire to work in Basketball Operations long-term
- Experience with basketball data sources and their nuances
- Work that's been used to make a real decision (team, front office, or published research)
- B2B SaaS or analytics product experience
- Experience in fast-moving, small-team environments
Company Overview