Tendo

Principal Data Scientist

Tendo

full-time

Posted on:

Origin:  • 🇺🇸 United States • California, Illinois, Pennsylvania, Tennessee, Utah

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Salary

💰 $157,250 - $212,750 per year

Job Level

Lead

Tech Stack

ETLPythonSaltStackSQL

About the role

  • 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.