Data Scientist – Scientific AI, Life Sciences Full-time

Your Growth

You’ll be working in one of our offices in North America in our Life Sciences practice.

You will work with cutting edge AI teams on research and development topics across our life sciences, global energy and materials (GEM), and advanced industries (AI) practices, serving as a data scientist in a technology development and delivery capacity.

You will be on McKinsey’s global scientific AI team helping to answer industry questions related to how AI can be used for therapeutics, chemicals & materials (including small molecules, proteins, mRNA, polymers, etc.).

In this role you will support the manager of data science on the development of data science and analytics roadmap of assets across cell-level initiatives. You will deliver distinctive capabilities, models, and insights through your work with client teams and clients.

Your Impact

Your role will be split between developing new internal knowledge, building AI and machine learning models & pipelines, supporting client discussions, prototype development, and deploying directly with client delivery teams. You will bring distinctive statistical, machine learning, and AI competency to complex client problems.

With your expertise in advanced mathematics, statistics, and/or machine learning, you will help build and shape McKinsey’s scientific AI offering. As a Data Scientist, you will play a pivotal role in the creation/dissemination of cutting-edge knowledge and proprietary assets. You will work in a multi-disciplinary team and build the firm’s reputation in your area of expertise.

You will ensure statistical validity and outputs of analytics, AI/ML models and translate results for senior stakeholders. You will write optimized code to advance our Data Science Toolbox and codify analytical methodologies for future deployment.

Your qualifications and skills

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