Senior Data Scientist — AI in Life Sciences

McKinsey & Company; Chicago (IL); Presencial; USD 176.000 - 179.500; Jornada completa

Descripción de la vacante

McKinsey & Company in the United States seeks a Senior Data Scientist to develop AI/ML models, advance analytics, and lead data science efforts within the Life Sciences practice in North America. Candidates will collaborate with client delivery teams, produce production-ready solutions, and contribute to McKinsey's scientific AI offerings.

You will work across disruptive AI topics, translate complex results for stakeholders, and drive end-to-end ownership of independent workstreams in a

Formación

• Master's degree with 5+ years or PhD degree with 2+ years of relevant experience in statistics, mathematics, computer science, or equivalent research experience.
• Machine learning 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.
• Strong programming experience in Python (R, C++ optional) and relevant analytics libraries (pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain).
• Proven experience applying machine learning techniques to solve business problems.
• Experience with version control (GitHub).
• Ability to write production code and object‑oriented programming.
• Proven track record of end‑to‑end ownership of independent workstreams.
• Good presentation and communication skills, with the ability to explain complex analytics concepts to non‑technical stakeholders.
• Proven experience advising external parties and/or functions.
• Exceptional time management to meet responsibilities in a complex and largely autonomous work environment.

Responsabilidades

• Develop new internal knowledge, build AI and machine learning models & pipelines, support client discussions, prototype development, and deploy directly with client delivery teams.
• Bring distinctive statistical, machine learning, and AI competency to complex client problems and help build and shape McKinsey's scientific AI offering.
• Create and disseminate cutting‑edge knowledge and proprietary assets, work in a multi‑disciplinary team, and build the firm's reputation in the area of expertise.
• Ensure statistical validity of analytics, AI/ML models, and translate results for senior stakeholders.
• Write optimized code to advance our Data Science Toolbox and codify analytical methodologies for future deployment.
• Work in one of our offices in North America within the Life Sciences practice as part of McKinsey's global scientific AI team, helping answer industry questions related to AI use in therapeutics, chemicals & materials, including small molecules, proteins, mRNA, polymers, etc.
• Collaborate with cutting‑edge AI teams on research and development topics in a start‑up‑like environment, serving as a Senior Data Scientist in a technology development and delivery capacity.
• Support the manager of data science in developing a data science and analytics roadmap of assets across cell‑level initiatives, delivering distinctive capabilities, models, and insights through work with client teams and clients.
• Flexibility to hire at the Senior Data Scientist I or II level, depending on experience.

Conocimientos

Statistics

Machine learning

Python

GitHub

Production code

End-to-end ownership

Communication

Advising

Educación

Master's degree

PhD

Herramientas

pandas

numpy

matplotlib

scikit-learn

PyTorch

TensorFlow

LangChain

Descripción del empleo

McKinsey & Company in the United States seeks a Senior Data Scientist to develop AI/ML models, advance analytics, and lead data science efforts within the Life Sciences practice in North America. Candidates will collaborate with client delivery teams, produce production-ready solutions, and contribute to McKinsey's scientific AI offerings.

You will work across disruptive AI topics, translate complex results for stakeholders, and drive end-to-end ownership of independent workstreams in a