About the position
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 Principal Data Scientist in a technology development and delivery capacity. You will lead a team of cross functional technologists and practitioners to innovate alongside clients and client service teams to develop new solutions or enhance existing assets. This will involve leveraging advanced mathematics, stats and machine learning to derive structure and knowledge from raw data sets across various industry sectors that will enable solutions for many clients and amplify impact across a broad range of issues. You will mentor data scientists across multiple projects, ensuring statistical validity, training data, and overall analytics methodological approach align with client and asset development objectives. You will be part of the 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 lead the development and own the data science and analytics roadmap of client-facing analytics assets across a broad range of topics. 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 through part-time staffing on clients. With your expertise in advanced mathematics, statistics, and/or machine learning, you will help build and shape McKinsey's scientific AI offering. As a Principal 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 act as an analytics translator with senior stakeholders while also advising clients throughout the stages of model development. You will help build our teams by collaborating with others across the practice and beyond as well as mentoring junior colleagues.
Responsibilities
- Lead a team of cross functional technologists and practitioners to innovate alongside clients and client service teams.
- Develop new solutions or enhance existing assets using advanced mathematics, statistics, and machine learning.
- Mentor data scientists across multiple projects, ensuring statistical validity and alignment with client objectives.
- Lead the development and own the data science and analytics roadmap of client-facing analytics assets.
- Support client discussions, prototype development, and deploy directly with client delivery teams.
- Act as an analytics translator with senior stakeholders and advise clients throughout the stages of model development.
Requirements
- Master's degree with 8+ years or PhD degree with 7+ years of relevant experience in statistics, mathematics, computer science, or equivalent experience with experience in research.
- Thought leadership or people leadership experience (e.g. managed project teams or direct reports).
- Experience in client delivery with direct client contact.
- 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 and 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.
