GenAI Health Data Scientist & Insights Lead

This role will develop and deploy generative artificial intelligence and machine learning (GenAI/ML) models from Walgreens real-world data (RWD) warehouses using a full suite of data science platform technologies. As a Health Data Researcher, this role will collaborate closely with data, technology, and business teams to develop intelligent solutions for healthcare and life science clients. As a member of the Walgreens Data & Insights group, this highly visible role will influence next generation solution design and roadmap decisions as part of Walgreens generative AI/machine learning commercialization strategy. Role will serve as a critical member of client delivery teams that will actively lead technical sessions as part of solution pilots and participate in generative AI/machine learning advisory workshops as a data science subject matter expert. Job Responsibilities Lead generative AI and machine learning project teams through the model development lifecycle across data pre-processing, model building, model evaluation, and model serving stages. Data Pre-Processing: Configure data connections/APIs, tokenize text strings, managed data in Azure Data Lake Storage / Snowflake. Model Building: Serve data to feature store, create training sets, push training data to Spark dataframes, write utilities to save performance metrics resulting from model training loops; Model Evaluation: Select and visualize model performance metrics (Precision vs. Recall, AUC), post-deployment model drift, and model output against ground truth baselines; Model Serving: Create Predict endpoints, run/deploy models to TorchScript, oversee REST endpoint model deployment, configure tracking servers Collaborate with Walgreens business leads and client points-of-contacts to perform opportunity discovery (e.g., product-solution fit evaluations) to close client strategic analytics gaps with Walgreens Data & Insights advanced analytics solutions. Coordinate client-specific data requirements, PHI/PII handling protocols, and data exchange action plans to support solution development blueprints. Partner with Walgreens technical teams to architect, maintain, and scale a fully-functioning data science workbench across Snowflake and DataBricks platforms that enables common model training, performance evaluation, and deployment tasks. Help define the Walgreens Data & Insights generative AI / machine learning use case portfolio and track delivery progress from inception through to deployment. Prioritize release of new solutions to initiate pilot programs with Pharma and Health Payer client teams. Maintain a high level of proficiency in the latest AI algorithms, libraries, and related technologies. Serve as a subject matter expert in vendor selection and evaluation as use cases call for additions to the Walgreens Data Science infrastructure and tool set. Develop project materials that effectively translate technical concepts and outcomes for business audiences to facilitate swift decision making. (e.g, presentations, white papers, industry webinars) Personify Walgreens 4C’s culture when collaborating with internal teams, stakeholders, and customers About Walgreens and WBA Walgreens ( is included in the U.S. Retail Pharmacy and U.S. Healthcare segments of Walgreens Boots Alliance, Inc. (Nasdaq: WBA), an integrated healthcare, pharmacy and retail leader with a 170 year heritage of caring for communities. WBA’s purpose is to create more joyful lives through better health. Operating nearly 9,000 retail locations across America, Puerto Rico and the U.S. Virgin Islands, Walgreens is proud to be a neighborhood health destination serving nearly 10 million customers each day. Walgreens pharmacists play a critical role in the U.S. healthcare system by providing a wide range of pharmacy and healthcare services, including those that drive equitable access to care for the nation’s medically underserved populations. To best meet the needs of customers and patients, Walgreens offers a true omnichannel experience, with fully integrated physical and digital platforms supported by the latest technology to deliver high-quality products and services in communities nationwide. The actual salary an employee can expect to receive, plus bonus pursuant to the terms of any bonus plan if applicable, will depend on experience, seniority, geographic location, and other factors permitted by law. To review benefits, please visit jobs.walgreens.com/benefits. An Equal Opportunity Employer, including disability/veterans. Basic Qualifications Bachelor's degree and at least 4 years of experience in quantitative or computational functions; or graduate degree in a quantitative, computational or technical discipline. Experience working with healthcare and/or pharmacy data sets in Health Information Trust Alliance (HITRUST) certified environments (e.g., encryption tools, dynamic masking policies, tokenization). Working knowledge of common machine learning language and libraries such as SQL, Python, PySpark, DASK, PyTorch, Tensorflow, Keras in a production coding environment. Demonstrable project experience in the areas of machine learning, ensemble methods, data mining, and time series forecasting. Configuring and deploying logically-separated data environments using machine learning services that include but are not limited to Azure Data Factory, Microsoft Fabric, Databricks, or Snowflake. Excellent organizational, planning, and project management skills with strong attention to detail and ability to effectively manage cross-functional projects. Experience establishing and maintaining key relationships with internal (peers, business partners and leadership) and external (business community, clients and vendors) within a matrix organization to ensure quality standards of service are met and collaborative problem solving is practiced. Strong oral and written communication skills. Willing to travel up to/at least 10% of the time for business purposes (within state and out of state). Preferred Qualifications Graduate degree in Data Science, Computer Science, Health Informatics, or other quantitative field or related work experience. Familiarity applying Responsible AI fairness practices and evaluation systems to train generative AI models using unbiased data sets. Working knowledge of standard healthcare data sources, e.g., Symphony patient claims data and sub-national data, IQVIA National Prescription data, CMS Medicare CCLF claims files, and Medicare Part D drug claims files. #J-18808-Ljbffr