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
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