Principal Data Scientist
Team: Engineering
Location: Salt Lake City, UT
Commitment: Full Time
Workplace Type: remote
Responsibilities
- Analyze data from multiple databases to drive optimization and improvement of quality outcomes, resource utilization, and risk adjustment.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize patient outcomes, patient experiences, risk adjustment opportunities, and other business outcomes.
- Explore and experiment with emerging AI technologies to evaluate their applicability in solving healthcare problems and improving operational workflows.
- Develop analytic data sets and use statistical software to analyze data sets as requested.
- Use third party software tools in the development of queries and visualizations.
- Coordinate with different functional teams to implement models and monitor outcomes.
Requirements
- Bachelor’s degree in data science, statistics, epidemiology, engineering, information science, computer science, OR equivalent technical experience.
- Hands-on experience writing Python code including, but not limited to, machine learning, data science and engineering, and ETL pipelines.
- 7+ years of experience in data analysis software, with data science experience preferred.
- 7+ years of experience with GitHub/Git, Python, SQL, statistics, and ML modeling.
- Track record of applying AI or ML models to solve practical, real-world problems—ideally in healthcare or similar complex domains.
- Knowledge of statistical concepts and data mining methods such as: Hypothesis testing (or A/B testing), distribution analysis, Bayesian estimation, Linear and Logistic Regression, GLMs, text mining, time series analysis, etc.
- Knowledge of a variety of traditional machine learning techniques such as: feature engineering methods for large scale numerical and categorical data, dimensionality reduction, clustering, Decision Trees/Random Forests/Gradient Boosted Decision Trees, Deep Learning.
- Knowledge of machine learning implementation strategies such as: proper and thorough evaluation of ML models in production, detecting data/covariate/concept drift, leveraging feature stores and model registries, deploying models as REST APIs, integrating models into products, etc.
- Interest in staying current on AI advancements (e.g., generative AI, LLMs, foundation models), and enthusiasm for integrating new capabilities into analytical workflows.
- Demonstrated proficiency in writing SQL queries on large, complex datasets for data analysis and analytics engineering.
- Strong problem-solving skills with an emphasis on data analytics.
- Excellent written and verbal communication skills for coordinating across teams.
Nice to Have
- Experience in the healthcare setting preferred.
- Experience with Epic EMR data preferred, but not required.
- Experience with healthcare financials/claims preferred, but not required.
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