Consultant Data Scientist

Blue Shield of CA

United States

United States

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Über

Your Role The Advanced Analytics team collaborates with various units within Blue Shield of CA to drive impactful business outcomes through the innovative use of AI, machine learning, and advanced statistical methods. As a Consultant Data Scientist, you will report directly to the Director of Advanced Analytics. Your responsibilities will include tackling diverse challenges such as analyzing text data from customer feedback, predicting clinical disease progression, assessing the effects of population health programs, identifying member behavior patterns, crafting propensity models, and conducting geospatial analyses to reveal social determinants of health. Your Knowledge and Experience A bachelor’s degree in mathematics, statistics, computer science, or a related quantitative field is required. At least 3 years of professional experience in Data Science or Machine Learning is required; alternatively, a Ph.D. in operations research, applied statistics, data mining, machine learning, or a similar quantitative discipline is acceptable. You must showcase your ability to convert business challenges into machine learning tasks and effectively communicate recommendations in a way that resonates with non-technical stakeholders. Proficiency in scalable data transformation techniques using SQL, SAS, Spark, or similar tools is essential. Strong skills in open-source programming languages such as Python, R, and Julia are necessary. Hands-on experience with cloud platforms like Azure and Google Cloud is crucial; familiarity with DataBricks would be beneficial. A solid understanding of statistical methodologies and advanced modeling techniques (e.g., SVM, K-Means, Random Forest, Boosting, Bayesian inference, natural language processing) is required. Practical experience with machine learning and deep learning libraries such as scikit-learn, XGBoost, Tensorflow, or PyTorch is essential. Knowledge in evaluating solution fairness, bias, accuracy, drift, validity, fit, robustness, and explainability is important. MLOps experience, including effective design documentation, unit and integration testing, and version control (git), is invaluable. Experience in experimental design and A/B testing is preferred. Familiarity with Generative AI techniques and applications, including natural language generation, image synthesis, and automated content creation, is desired. Experience with Generative AI frameworks and tools such as GPT, GANs, and VAEs is advantageous. The ability to develop and deploy generative models for various use cases is essential. An understanding of ethical considerations and best practices in Generative AI development and deployment is necessary. A collaborative spirit with the capability to partner with and lead diverse stakeholders across different functions and experience levels is key.

United States

Sprachkenntnisse

English

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