About the role
This is a fully remote role within the United States. We’re looking for a driven, entrepreneurial Data Science Engineer to join our team and help eliminate the financial complexity of healthcare. You'll join our Data Science Engineering team supporting analytics, partnering closely with Product, Engineering, and business stakeholders to help Turquoise grow and scale our impact by making better decisions with data. From defining product metrics and measuring customer engagement to enabling self-service analytics and evaluating business initiatives, you'll build the 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 What you’ll bring to
the role 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
