Data Science Engineer, Analytics

Turquoise Health is a Series C price transparency platform for finance leaders across healthcare. They are seeking a driven, entrepreneurial Data Science Engineer to join their team and support analytics by building datasets, pipelines, dashboards, and analyses that transform data into actionable insights across the company.

Responsibilities:

• Partner cross-functionally with Product, Engineering, and business stakeholders to define metrics, measure outcomes, and evaluate impact
• Build and maintain data pipelines, data models, dashboards, and analytical infrastructure that support product, operational, and strategic decision-making
• Conduct analyses to understand product adoption, customer engagement, business performance, and operational efficiency
• Contribute to the development of analytics best practices, shared datasets, and company-wide metrics
• Seek and act on feedback from internal stakeholders; iterate quickly with an eye toward value

Requirements:

• Bachelor's degree or equivalent experience. Non-traditional backgrounds welcome
• 2+ years developing data models, pipelines, and end-to-end analytical solutions in Python and SQL. Comfortable with OOP and functional patterns, code organization beyond scripts, and debugging workflows
• Experience with dataframe libraries (pandas, polars)
• Experience with ETL/ELT workflows and orchestration (Airflow, dbt)
• Comfort with cloud services (AWS S3, EC2, RDS)
• Ability to design data systems with scalability, performance, and cost efficiency in mind, particularly for compute- and data-intensive workloads
• Entrepreneurial mindset: you prioritize tasks with an eye for evolving business needs
• Comfortable working remotely in a collaborative, technical team
• Experience with b2b healthcare product analytics
• Experience applying quantitative methods (regression, causal inference) to product or business questions
• Thoughtful use of AI coding agents and LLMs in development workflows