Applied Data Scientist

ABOUT US

A Specialty Path to Good Health

Upperline Health is the nation's largest provider group dedicated to lower extremity, wound and vascular care. Founded in 2017 with the ambitious goal of transforming specialty care, Upperline Health is a pioneer in bringing value-based care models into specialty care. Upperline delivers a more efficient path for patients to receive consistent and effective treatment for chronic illnesses.

Triage is temporary.

Treatment is transformative.

Upperline Health providers coordinate patients' care among a team of specialists -- physicians, advanced practice providers, care navigators, pharmacists, dieticians, and social workers for integrated treatment that addresses patients' immediate and long-term health needs.

We put patients at the center of value-based care.

About the Applied Data Scientist, Model Pipelines

We are looking for a talented Applied Data Scientist to build, validate, deploy, and maintain predictive healthcare models. You will build the feature and model pipelines that turn claims and EHR data into predictions people use (risk stratification, utilization, expense/revenue projection, outreach prioritization) and own them from framing through monitoring. This role is ideal for someone with strong data engineering skills and the modeling depth to build, validate, and defend a model, including looking back at how past predictions performed.

What You'll Do

• Design and implement data pipelines using SQL and Python (Databricks) to synthesize large medical claims and EHR records into usable data features on governed data
• Build, refine, and validate machine learning models in Python, with attention to performance, calibration, bias, and drift, in collaboration with analysts and engineers
• Conduct lookback analyses: backtest model predictions against historical claims and outcomes, and measure whether outreach or interventions changed results
• Deploy models through shared engineering tooling, manage model artifacts, then monitor and retrain
• Ensure data quality, reproducibility, and performance in the pipelines
• Contribute to best practices for data governance and model deployment
• Explain results to non-technical leaders and tie each to an action

Qualifications of the Applied Data Scientist, Model Pipelines

• Degree in Data Science or a related quantitative field (e.g., Statistics, Computer Science, Applied Mathematics, Biostatistics); Master's in Data Science preferred
• 2-5+ years of experience as a Data Scientist, Machine Learning Engineer, Data Engineer, or related role, with at least one model in production and a focus on building and maintaining production-quality data pipelines and machine learning infrastructure
• Proficiency in SQL/Databricks and Python; willingness to learn SAS as needed for dataset generation (prior experience in SAS is not required)
• Experience with lookback (retrospective) analysis and backtesting, and evaluating model performance, calibration, bias, and drift
• Experience building scalable data pipelines, working with large datasets, and transforming raw data into reliable features for machine learning workflows
• Strong working knowledge of machine learning workflows (e.g., scikit-learn, pandas, NumPy) and Git, with judgment on when a simpler model is better
• Strong problem-solving and communication skills, sharp critical thinking skills, and a keen eye for details
• Comfortable with a fast-paced workstream that requires flexibility to work on multiple projects or analyses at the same time

Nice to Have

• Background in Healthcare Analytics, experience with Healthcare Claims Data (e.g., Medicare Claims), ACO REACH, or risk adjustment
• Experience with cloud platforms (e.g., AWS, Azure), databricks orchestration, and EMRs (e.g., athenahealth)
• Projects (personal or work) related to building + deploying machine learning models to answer specific business-related questions

Benefits

Comprehensive benefit options include medical, dental and vision, 401K and PTO.

Compensation

Compensation is commensurate to compensation for similar positions in the region and based on prior training and experience

Job Type: Full-time