Accelerate business outcomes through the application of AI/machine learning, statistical methodologies, or unstructured data analysis techniques to uncover insights, predict behaviors, and ultimately drive automation to create “intelligence at scale”.
Requirements
- Proficiency in scalable data transformation techniques using SQL, SAS, Spark, or equivalent
- Proficiency in open-source languages such as Python, R, and Julia
- Hands-on experience with cloud environments such as Azure and Google Cloud and nice to have experience in DataBricks
- Understanding of statistical methods and advanced modeling techniques (e.g., SVM, K-Means, Random Forest, Boosting, Bayesian inference, natural language processing)
- Experience with machine learning and deep learning packages (scikit-learn, XGBoost, Tensorflow, or PyTorch)
- Knowledge of evaluating solutions for fairness, bias, accuracy, drift, validity, fit, robustness, and explainability
- MLOps experience including good design documentation, unit testing, integration testing, and version control (git)
Responsibilities
- Perform data exploration and visualization using Python on DataBricks, Tableau, and Python Notebooks to understand the signal-to-noise ratio in datasets
- Partner with the data engineering team for rapid prototyping of training data sets using SQL, Apache Spark, and other tools
- Conduct feature engineering using appropriate techniques for the given data and business problem
- Develop robust model validation procedures and generate performance metrics for evaluation and monitoring
- Act as the subject matter expert in applied ML for claims, premiums, member risk scores, and other business contexts
- Drive the lifecycle of machine learning projects from ideation to deployment, ensuring timely delivery and maintaining documentation
- Convert text-based data into feature data sets for predictive analytics
Other
- Requires a bachelor’s degree in mathematics, statistics, computer science, or an equivalent quantitative scientific discipline
- Requires at least 3 years of professional Data Science or ML experience; or a Ph.D. degree in operations research, applied statistics, data mining, machine learning, or other quantitative discipline
- Must be able to demonstrate real-world experience to translate business problems into an ML problem and be able to communicate AI recommendations in a business context to a general non-technical audience
- Ability to partner, collaborate with, and lead relevant stakeholders across diverse functions and experience levels
- External hires must pass a background check/drug screen.
