Job Specifications
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. Responsibilities
Lead the Development of Innovative AI solutions: Be responsible for designing, implementing, and scaling sophisticated AI solutions that address key business challenges within the healthcare industry by leveraging your expertise in areas such as Machine Learning, Generative AI, and RAG Technologies.
Develop advanced ML models for forecasting, classification, risk prediction, and other critical applications.
Explore and leverage the latest Generative AI (GenAI) technologies, including Large Language Models (LLMs), for applications like summarization, generation, classification and extraction.
Build robust Retrieval Augmented Generation (RAG) systems to integrate LLMs with vast repositories of healthcare and business data, ensuring accurate and relevant outputs.
Shape Our AI Strategy: Work closely with key stakeholders across the organization to understand their needs and translate them into actionable AI-driven or AI-powered solutions.
Act as a champion for AI within Cardinal Health, influencing the direction of our technology roadmap and ensuring alignment with our overall business objectives.
Guide and mentor a team of Data Scientists and ML Engineers by providing technical guidance, mentorship, and support to a team of skilled and geographically distributed data scientists, while fostering a collaborative and innovative environment that encourages continuous learning and growth.
Embrace a AI-Driven Culture: foster a culture of data-driven decision-making, promoting the use of AI insights to drive business outcomes and improve customer experience and patient care. Qualifications
8-12 years of experience with a minimum of 4 years of experience in data science, with a strong track record of success in developing and deploying complex AI/ML solutions, preferred
Bachelor's degree in related field, or equivalent work experience, preferred
GenAI Proficiency: Deep understanding of Generative AI concepts, including LLMs, RAG technologies, embedding models, prompting techniques, and vector databases, along with evaluating retrievals from RAGs and GenAI models without ground truth
Experience working with building production ready Generative AI Applications involving RAGs, LLM, vector databases and embeddings model.
Extensive knowledge of healthcare data, including clinical data, patient demographics, and claims data. Understanding of HIPAA and other relevant regulations, preferred.
Experience working with cloud platforms like Google Cloud Platform (GCP) for data processing, model training, evaluation, monitoring, deployment and support preferred.
Proven ability to lead data science projects, mentor colleagues, and effectively communicate complex technical concepts to both technical and non-technical audiences preferred.
Proficiency in Python, statistical programming languages, machine learning libraries (Scikit-learn, TensorFlow, PyTorch), cloud platforms, and data engineering tools preferred.
Experience in Cloud Functions, VertexAI, MLFlow, Storage Buckets, IAM Principles and Service Accounts preferred.
Experience in building end-to-end ML pipelines, from data ingestion and feature engineering to model training, deployment, and scaling preferred.
Experience in building and implementing CI/CD pipelines for ML models and other solutions, ensuring seamless integration and deployment in production environments preferred.
Familiarity with RESTful API design and implementation, including building robust APIs to integrate your ML models and GenAI solutions with existing systems preferred.
Working understanding of software engineering patterns, solutions architecture, information architecture, and security architecture with an emphasis on ML/GenAI implementations preferred.
Experience working in Agile development environments, including Scrum or Kanban, and a strong understanding of Agile principles and practices preferred.
Familiarity with DevSecOps principles and practices, i