Lead Data Scientist - AgenticAI

McKesson•Mississauga, ON

1d•$122,100 - $162,800

About The Position

McKesson Corporation is seeking a highly skilled and innovative Lead Data Scientist with experience on Generative AI development. This role will lead the design, development, and deployment of cutting-edge ML & GenAI solutions, leveraging advanced machine learning techniques to solve complex healthcare challenges and drive business transformation.

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) and MLOps practices.
Ability to clearly communicate complex technical concepts to diverse business and technical audiences.
Demonstrated leadership in driving projects, influencing stakeholders, and mentoring team members.

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.