Staff Data Scientist | ML
Staff Data Scientist on the Pay team building and productionizing ML/LLM models to detect erroneous healthcare claims payments. Owns end-to-end data science work, leads high-impact initiatives, and sets technical direction.
About the job
What You'll Do
- Solve real business problems with data: Own end-to-end data science work, from problem framing and data exploration to modeling, validation, and production impact.
- Lead high-impact initiatives: Drive complex efforts such as vendor leakage detection and prevention, delivering measurable revenue or cost improvements.
- Innovate on methods and approaches: Go beyond standard analyses by developing new metrics, models, or workflows when existing approaches fall short.
- Build and productionize models: Design, build, and support production ML or LLM-powered solutions in collaboration with engineering and product partners.
- Work deeply with data: Use SQL fluently to explore large datasets, build reliable data assets, and validate results.
- Influence cross-functionally: Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans.
- Set technical direction (especially at L7): Raise the bar for data science quality, scalability, and impact across the organization through mentorship, design reviews, and technical leadership.
What You'll Bring
- Proven track record of hands-on data science impact on real-world problems, not just research or dashboards.
- Strong proficiency in SQL, with experience working directly on complex, large-scale datasets.
- Experience building, shipping, or supporting production ML systems, preferably some exposure to LLM-based products or workflows.
- Ability to independently scope ambiguous problems, identify the right data and methods, and drive work to completion.
- Strong communication skills, with the ability to explain complex analyses and models to non-technical stakeholders.
Skills
SQL, Machine Learning, LLMs, Python, Data Exploration, Data Modeling, Production Ml Systems, Data Validation, Large-Scale Datasets, Cross-Functional Collaboration
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