Senior Data Scientist

Overview

What Data Science contributes to Cardinal Health

The Data & Analytics Function oversees the analytics life-cycle in order to identify, analyze and present relevant insights that drive business decisions and anticipate opportunities to achieve a competitive advantage. This function manages analytic data platforms, the access, design and implementation of reporting/business intelligence solutions, and the application of advanced quantitative modeling.

Data Science applies base, scientific methodologies from various disciplines, techniques and tools that extracts knowledge and insight from data to solve complex business problems on large data sets, integrating multiple systems.

At Cardinal Health s Artificial Intelligence Center of Excellence (AI CoE), we are pushing the boundaries of healthcare with cutting-edge Data Science and Artificial Intelligence (AI). Our mission is to leverage the power of data to create innovative solutions that improve patient outcomes, streamline operations, and enhance the overall healthcare experience.

• Lead the Development of Innovative AI solutions: design, implement, and scale sophisticated AI solutions addressing key healthcare business challenges by leveraging expertise in Machine Learning, Generative AI, and RAG Technologies.
• Develop advanced ML models for forecasting, classification, risk prediction, and other critical applications.
• Explore and leverage Generative AI (GenAI) technologies, including Large Language Models (LLMs), for summarization, generation, classification and extraction.
• Build robust Retrieval Augmented Generation (RAG) systems to integrate LLMs with large data repositories, ensuring accurate and relevant outputs.
• Shape Our AI Strategy: collaborate with stakeholders to understand needs and translate them into AI-driven solutions.
• Act as a champion for AI within Cardinal Health, influencing the technology roadmap and aligning with business objectives.
• Guide and mentor a team of Data Scientists and ML Engineers, fostering a collaborative and innovative environment that supports continuous learning and growth.
• Embrace an AI-driven culture: promote data-driven decision-making to improve outcomes and patient care.

• 8-12 years of experience with a minimum of 4 years in data science, with a track record of success in developing and deploying complex AI/ML solutions
• Bachelor s degree in related field, or equivalent work experience
• GenAI Proficiency: deep understanding of Generative AI concepts, including LLMs, RAG technologies, embedding models, prompting techniques, and vector databases; ability to evaluate retrievals from RAGs and GenAI models
• Experience building production-ready Generative AI applications involving RAGs, LLMs, vector databases and embeddings models
• Extensive knowledge of healthcare data, including clinical data, patient demographics, and claims data; understanding of HIPAA and related regulations
• Experience with cloud platforms like Google Cloud Platform (GCP) for data processing, model training, evaluation, monitoring, deployment and support
• Proven ability to lead data science projects, mentor colleagues, and communicate complex concepts to technical and non-technical audiences
• Proficiency in Python, statistical programming languages, machine learning libraries (Scikit-learn, Tensor Flow, PyTorch), cloud platforms, and data engineering tools
• Experience with Cloud Functions, Vertex AI, MLflow, Storage Buckets, IAM Principles and Service Accounts
• Experience building end-to-end ML pipelines from data ingestion to model deployment and scaling
• Experience implementing CI/CD pipelines for ML models and related solutions for production environments
• Familiarity with RESTful API design and implementation to integrate ML models and GenAI solutions with existing systems
• Understanding of software engineering patterns, architecture, information architecture, and security architecture with emphasis on ML/GenAI
• Experience working in Agile environments (Scrum or Kanban) and understanding of Agile principles
• Familiarity with Dev Sec Ops  principles and security considerations across the development lifecycle