Tendo analytics projects focused on quality management and risk adjustment. The person in this role will access data from multiple sources (public and private) and translate it into information that is meaningful and actionable for health systems. The Principal Data Scientist develops, maintains, and enhances predictive models that improve products in several areas including, but not limited to quality, risk, and operational efficiency.
Requirements
- Hands-on experience writing Python code including, but not limited to, machine learning, data science and engineering, and ETL pipelines.
- 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.
- Demonstrated proficiency in writing SQL queries on large, complex datasets for data analysis and analytics engineering.
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.
Other
- Bachelor’s degree in data science, statistics, epidemiology, engineering, information science, computer science, OR equivalent technical experience.
- 7+ years of experience in data analysis software, with data science experience preferred.
- Interest in staying current on AI advancements (e.g., generative AI, LLMs, foundation models), and enthusiasm for integrating new capabilities into analytical workflows.
- Strong problem-solving skills with an emphasis on data analytics.
- Excellent written and verbal communication skills for coordinating across teams.
