Data Scientist, Consultant

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, Consultant 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.

• 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
• 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 Data Bricks
• 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)
• Experience in experimentation design and A/B testing
• 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 such as GPT, GANs, and VAEs
• 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
• Ability to partner, collaborate with, and lead relevant stakeholders across diverse functions and experience levels