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Senior Data Scientist - Scientific AI, Life Sciences...

McKinsey & Company, Inc.
London
1 month ago
Applications closed

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Your Growth You will be working in our London office
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 Senior 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 Senior 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 degree with 5+ years or PhD degree with 2+ years of
    relevant experience in statistics, mathematics, computer science,
    or equivalent experience with experience in research - 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
    #J-18808-Ljbffr
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