Data Scientist | Modeling
Develop and advance ML models to select healthcare claims for auditing, improving precision and recovering millions in overpayments for large health plans. Requires experience with SQL, Python/R, and building modern ML models from scratch.
About the job
What You’ll Do
- Master Claim Audits: Dive into various clinical and coding audits, unpacking the data that drives audit decisions, and understanding past outcomes.
- Model Advancement: Curate labeled data, hypothesize new features, and develop ML models that target claims for audit with even greater precision.
- Optimize Impact: Measure model performance rigorously, translating outcomes into operational insights and recommendations for our clients.
- Tame Complexity: Work with vast, nuanced data pipelines derived from intricate business workflows—often unclean and undefined—to refine them for robust modeling.
- Drive Team Success: Enhance data pipelines, modeling infrastructure, and team tools to level up our capabilities and decision-making efficiency.
- Grow with Healthcare: Build expertise in healthcare data and make an impact on the industry. Engage with clients and internal experts to refine model insights, ensuring alignment with real business needs.
What You Bring
- Enjoy solving real-world business problems involving data-driven optimization and ML modeling - and have been doing that successfully for a while.
- Comfortable measuring and optimizing the direct business impact of work.
- Experienced with SQL, handling large-scale data, and comfortable with at least one programming language (Python, R, etc.).
- Experience building ML models using modern ML approaches like Neural Nets or Tree-ensembles from scratch for new applications - making decisions relating to which supervised labels to use, the metric to optimize for, and the features likely to be useful.
What We Offer
- Work from anywhere in the US!
- Salary range from $180k-$230k+ based on level and experience, plus equity, healthcare, unlimited PTO, and more.
Skills
SQL, Python, R, Machine Learning, Neural Networks, Tree Ensembles, Data Pipelines
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