Data Scientist – Scientific AI

Overview

As a Data Scientist on McKinsey’s global scientific AI team, you apply advanced statistics and machine learning to complex client problems in life sciences, chemicals, and materials. You’ll develop new internal knowledge, build AI models and pipelines, and support client discussions with practical insights. Placed in London or Wroclaw, you collaborate across disciplines to shape McKinsey’s scientific AI offerings and deliver measurable impact. This role offers strong mentorship and a fast-paced learning culture to accelerate your growth as a leader.

Pay / Benefits

• competitive salary
• comprehensive benefits package
• mentorship and structured learning programs
• apprenticeship culture
• global community of colleagues
• development opportunities

Responsibilities

• Develop and disseminate new knowledge and proprietary assets
• Build AI/ML models and data pipelines; prototype development and client-delivery support
• Translate model outputs to senior stakeholders; ensure statistical validity
• Write production-ready code to advance Data Science Toolbox and codify methodologies
• Support data science roadmap across cell-level initiatives and cross-functional teams
• Collaborate in multi-disciplinary teams to strengthen firm capabilities in AI for life sciences, energy and materials

Key requirements

• Master’s or PhD degree
• 2+ years of relevant experience in statistics, mathematics, CS or equivalent with research background
• Proven experience applying machine learning to business problems
• Experience translating technical methods to non-technical stakeholders
• Strong programming in Python and libraries (pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain)
• GitHub or version control experience
• ML experience with causality, Bayesian stats, optimization, survival analysis, design of experiments, longitudinal analysis, surrogate models, transformers, Knowledge Graphs, Graph NNs, Deep Learning, computer vision
• Ability to write production code and OO programming

• ability to translate technical concepts to non-technical stakeholders
• strong collaboration across multidisciplinary teams
• ethics and integrity in problem-solving
• Python programming
• ML/AI development and deployment
• Statistical modeling and inference

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