Your Role
The Advanced Analytics team works in partnership across the entire enterprise to 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”. The Data Scientist, Principal will report to the Director, Advanced Analytics. In this role you will solve problems which range from but are not limited to, text analytics of customer feedback, conversations and clinical notes, predicting clinical disease progression, understanding the impact of population health programs, clustering member behaviors, creating propensity models, and geospatial analysis of populations to uncover social determinants of health.
Your Work
In this role, you will:
- Acts as the subject matter expert in the area of applied ML to claim, premium, member, risk scores, or other business relevant contexts
- Collaborate with product owners and business stakeholders to identify opportunities to optimize processes and decision-making. Evaluate business requirements and develop recommendations execution plan (i.e. CRISP-DM) if a machine learning solution is warranted. This includes and assumes the ability to create new, business relevant, health-care specific ML models
- Partner with product owners to drive lifecycle of machine learning project from ideation to model deployment. This includes and assumes the ability to delivers projects and models on time, as well as team-based accountability to maintain traceable documentation on ML models
- Perform scientific literature review to inform statistical modelling approaches and best practices to achieve model validity
- Translate business requirements into machine learning technical specifications, and partner with data engineers to design scalable pipelines based on defined requirements
- Help the team move from on-prem infrastructure to the cloud
- Translate text based data to feature data sets used to power our predictive analytics
- Anticipate and prevent problems and roadblocks before they occur
- Help the team establish a MLOps framework for managing applications
- Develop materials to explain project findings
- Mentor less experienced members of the team
- Other projects or duties as assigned.
Your Knowledge and Experience
- Requires college degree in mathematics, statistics, computer science or equivalent quantitative scientific discipline
- Requires a minimum of 6 to 7 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
- Be able to demonstrate real-world experience to translate business problems into ML problem
- Demonstrate ability to communicate AI-recommendations in a business-context to general non-technical audience
- High proficiency in scalable data transformation techniques using SQL, SAS, Spark or equivalent
- Expert in open-source languages such as Python, R, and Julia
- Hands on experience with cloud environments such as Azure and Google Cloud
- Understanding of statistical methods and advanced modeling techniques (e.g., SVM, K-Means, Random Forest, Boosting, Bayesian inference, natural language processing)
- Extensive experience with machine learning and deep learning packages (scikit-learn, XGBoost, Tensorflow or PyTorch)
- Experience evaluating solutions for fairness, bias, accuracy, drift, validity, fit, robustness and explainability
- Solid MLOps practices including good design documentation, unit testing, integration testing and version control (git)
- Proficient in experimentation design and A/B testing
- Ability to partner, collaborate with, and lead relevant stakeholders across diverse functions and experience levels
