Data Scientist, Principal

Your Role

The Advanced Analytics team works in partnership across the entire Blue Shield of CA 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:

• Act as the subject matter expert in the area of applied ML to claim, premium, member, risk scores, or other business relevant contexts
• 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
• Lead and contribute to Generative AI projects, including model development and deployment for applications like natural language generation and image synthesis.
• Stay updated with advancements in Generative AI and incorporate relevant techniques into projects
• Collaborate with cross-functional teams to integrate Generative AI solutions into products and services
• Conduct experiments and research to explore new applications of Generative AI in healthcare
• 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
• Ability 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
• Knowledge and experience in Generative AI techniques and applications, including natural language generation, image synthesis, and automated content creation.
• Experience with Generative AI frameworks and tools 
• Ability to develop and deploy generative models for various applications.
• Understanding of ethical considerations and best practices in the development and deployment of Generative AI solutions.
• Experience with agent-based technologies, including autonomous agents and orchestration frameworks like LangChain or Semantic Kernel.
• Ability to design and implement agent-based systems for complex problem-solving.
• Understanding of the principles and applications of agent-based modeling and simulation.
• Experience with LLM fine-tuning and Retrieval-Augmented Generation (RAG) techniques
• 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