Tendo - Principal Data Scientist

• 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. • 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. • Experience in the healthcare setting preferred. • Experience with Epic EMR data preferred, but not required. • Experience with healthcare financials/claims preferred, but not required.