Principal Data Scientist, Scientific AI – Life Sciences

McKinsey; Los Angeles (CA); On-site; USD 214,000 - 218,000; Full time

Benefits offered by this job

Comprehensive benefits

Retirement contributions

Job summary

McKinsey, a global leader in management consulting, seeks a Principal Data Scientist to join its Scientific AI team in North America, focused on Life Sciences. You will develop AI models, drive research, and collaborate with client delivery teams to translate insights into strategic actions.

You will mentor colleagues, shape internal knowledge, and present findings to executives, while ensuring rigorous statistical validity and scalable code. The role blends research with practical impact.

Qualifications

• Advanced degrees with 7+ years (PhD) or 8+ years (Master's) of relevant experience.
• Domain knowledge in Life Sciences.
• ML expertise: causality, Bayesian stats, transformers, graph networks.
• Strong coding ability in Python with production-grade practices.
• Experience leading teams and advising external parties.

Responsibilities

• Develop advanced models and AI capabilities for client delivery.
• Mentor and lead multidisciplinary teams, ensure project ownership.
• Translate analytics outputs for senior stakeholders and clients.
• Contribute to proprietary assets and internal knowledge creation.

Skills

Statistics

Mathematics

Machine learning

Python programming

Leadership

Git

Communication

Life Sciences domain knowledge

Education

Master's degree in statistics / mathematics / CS

PhD in a relevant field

Tools

Python (pandas, numpy)

scikit-learn

PyTorch / TensorFlow

Job description

McKinsey, a global leader in management consulting, seeks a Principal Data Scientist to join its Scientific AI team in North America, focused on Life Sciences. You will develop AI models, drive research, and collaborate with client delivery teams to translate insights into strategic actions.

You will mentor colleagues, shape internal knowledge, and present findings to executives, while ensuring rigorous statistical validity and scalable code. The role blends research with practical impact.