Lead Data Scientist at McKesson focusing on Generative AI and ML solutions. Address complex healthcare challenges and drive business transformation via innovative technology.
Responsibilities
Lead the end-to-end lifecycle of Generative AI and agentic AI solutions, including ideation, research, prototyping, implementation, evaluation, deployment, and production support
Architect and develop scalable GenAI systems such as LLM-based applications, Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation workflows to improve decision-making, operational efficiency, and customer outcomes
Drive adoption of best practices in prompt engineering, LLM evaluation, fine-tuning strategies, and secure model hosting where applicable
Apply advanced data science and machine learning techniques across a wide range of use cases including predictive modeling, forecasting, classification, anomaly detection, recommendation systems, NLP, and GenAI-enabled analytics
Develop custom ML models and analytical frameworks tailored to complex healthcare and enterprise data challenges
Perform exploratory data analysis (EDA), feature engineering, and statistical analysis to generate actionable insights and guide solution design
Collaborate with engineering and platform teams to deploy, monitor, and maintain ML and GenAI solutions in production environments using cloud-native and MLOps best practices
Establish model performance tracking, drift detection, reliability monitoring, and continuous improvement processes for deployed models and AI agents
Ensure solutions are scalable, cost-efficient, resilient, and aligned with enterprise architecture standards
Ensure strong model governance, documentation, and auditability across all ML and GenAI solutions
Apply Responsible AI principles including explainability, transparency, data privacy, security, and regulatory compliance, particularly within healthcare contexts
Provide guidance on safe, compliant, and ethical use of LLMs and agentic AI across enterprise use cases
Work closely with business stakeholders and product managers to translate requirements into clear analytical problem statements, solution designs, and execution roadmaps
Present technical solutions, insights, and progress updates to both technical and non-technical audiences, including senior leadership
Mentor and guide junior data scientists and engineers, fostering a culture of technical excellence, innovation, and continuous learning.
Requirements
Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Data Science, or a related quantitative field
7–10+ years of experience in data science or advanced analytics, with significant hands-on experience in Generative AI and LLM-based systems
Proven experience designing, building, and deploying production-grade ML and GenAI solutions, including LLMs, RAG architectures, and AI-driven automation
Strong proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, and/or Hugging Face
Deep understanding of machine learning algorithms, statistical modeling, experimental design, and data structures
Experience working with cloud platforms (Azure preferred; AWS/GCP acceptable)
Ability to clearly communicate complex technical concepts to diverse business and technical audiences
Demonstrated leadership in driving projects, influencing stakeholders, and mentoring team members.
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