Design and deploy predictive models that improve customer acquisition, conversion, and retention (e.g., lead scoring, customer health, product adoption forecasts)
Partner with Product, Revenue, and CX teams to identify opportunities where modeling and experimentation can drive measurable business outcomes
Build prototypes and production-ready models in Python, leveraging our modern data stack (Snowflake, dbt, etc.) for scalable development and deployment
Collaborate with data engineering to ensure clean, reliable, and extensible data pipelines for advanced analytics and ML use cases
Translate modeling outputs into clear recommendations and decision frameworks for senior leaders
Contribute to the design of analytics capabilities that can extend into our products, creating differentiated customer-facing insights
Requirements
5–7 years of experience in data science, applied machine learning, or advanced analytics
Strong SQL and Python skills
Experience with forecasting, classification, or scoring models applied in business contexts
Familiarity with modern data workflows and tools (dbt, Snowflake, Hex, Streamlit)
Strong communication skills, with ability to explain complex models and tradeoffs to non-technical stakeholders
Bonus: experience embedding ML insights into applications or customer-facing products