Principal Data Scientist – Scientific AI – Life Sciences
| Company | McKinsey & Company |
|---|---|
| Location | Boston, MA, USA, Seattle, WA, USA, Houston, TX, USA, Washington, DC, USA, San Francisco, CA, USA, Austin, TX, USA, Los Angeles, CA, USA, Jackson Township, NJ, USA, Philadelphia, PA, USA, Chicago, IL, USA, Charlotte, NC, USA, Pittsburgh, PA, USA, New York, NY, USA, Mountain View, CA, USA, Atlanta, GA, USA, Darien, CT, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
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 (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
Responsibilities
- 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
- Mentor data scientists across multiple projects, ensuring statistical validity, training data, and overall analytics methodological approach align with client and asset development objectives
- Lead the development and own the data science and analytics roadmap of client-facing analytics assets across a broad range of topics
- Split role between developing new internal knowledge, building AI and machine learning models & pipelines, supporting client discussions, prototype development, and deploying directly with client delivery teams
- Act as an analytics translator with senior stakeholders while also advising clients throughout the stages of model development
- Collaborate with others across the practice and beyond as well as mentoring junior colleagues
Preferred Qualifications
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No preferred qualifications provided.