• Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools. • Apply advanced data science techniques to identify patterns, inefficiencies, and bottlenecks across logistics and operations. • Build scalable data pipelines and analytical models using PySpark for large-scale datasets. • Develop predictive and prescriptive models to support decision-making in areas such as demand forecasting, routing, and inventory management. • Collaborate with cross-functional teams including operations, product, and engineering to translate business challenges into analytical solutions. • Communicate insights and recommendations clearly to stakeholders through data storytelling, visualizations, and presentations. • Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards. • Collaborate, coach, and learn with a growing team of experienced Data Scientists. • Stay connected with external sources of ideas through conferences and community engagements

• Oversee and manage an ever-changing portfolio of claim-centric algorithms that identify claims pre and post-pay • Responsible for providing depositions and testifying in court to support legal actions initiated by CareSource and the Attorney General • Collaborate with cross-functional teams to solve Program Integrity problems, develop new algorithms and models, and identify trends and opportunities • Lead a team of data scientists and statisticians to develop and drive innovative approaches to increase PI effectiveness and efficiency • Monitor and provide explanation of anomalies related to trends associated with the potential for Fraud Waste and Abuse across the corporate enterprise

• Spearhead the development of state-of-the-art natural language processing (NLP) and large language model (LLM) solutions • Mentor Associate Data Scientists in analyzing complex data • Perform independent and team-based research • Generate insights from large data sets • Interface with business leaders and data engineering teams • Contribute to model governance, documentation, and explainability frameworks

• Design and implement end-to-end ML pipelines • Develop and experiment with Deep Neural Network architectures • Translate ambiguous business requirements into technical solutions • Work with large datasets for acquisition, preprocessing, and analysis • Ensure code quality and maintainability • Collaborate with cross-functional teams to integrate ML solutions into production systems • Utilize CI/CD, version control, and automated testing in ML projects