WHOOP is seeking a Business Data Scientist II to join their Growth Analytics team to build statistical and machine learning models that help WHOOP grow its business by acquiring, engaging, retaining, and upselling members.
Requirements
- Strong statistical modeling and machine learning background, with experience in predictive modeling, classification, clustering, or recommendation systems.
- Proficiency in Python as the primary language for analysis and modeling, with strong SQL skills for working with large datasets.
- Ability to build reproducible, production-ready pipelines and experience collaborating with engineers and ML Ops teams to deploy and monitor models in production.
- Hands-on experience with experimentation frameworks (A/B testing, multi-armed bandits, causal inference) and interpreting results in a business context.
Responsibilities
- Build, validate, and iterate on statistical and machine learning models to solve core growth problems (e.g., churn prediction, personalization, bandit optimization).
- Drive novel model ideation and development, rapidly prototyping MVPs locally for testing and iteration.
- Partner with Software Engineers and ML Ops to deploy robust inference models into production, ensuring solutions are scalable, maintainable, and integrated into WHOOP’s digital experiences.
- Collaborate with Product Managers, Analysts, and Engineers to design, implement, and evaluate experiments that inform product and business strategy.
- Stay at the forefront of emerging technologies in AI, machine learning, and experimentation, applying them to WHOOP’s most impactful challenges.
- Communicate findings and recommendations clearly to technical and non-technical stakeholders, translating complex analyses into actionable insights.
Other
- Identify and scope opportunities where data science can drive member acquisition, engagement, retention, and upsell.
- This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
- Strong business acumen; able to translate ambiguous business problems into data science approaches.
- Comfort balancing rigor with pragmatism — prioritizing speed, MVPs, and iteration when driving growth.
- Excellent communication and collaboration skills.
