Data Scientist - Scientific AI, Life Sciences

McKinsey & Company, Inc.; London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An established industry player is seeking a Data Scientist to join their innovative team in London. In this role, you will work with cutting-edge AI technologies, addressing complex problems in life sciences and advanced industries. Your expertise in statistics and machine learning will be pivotal in developing models and insights that drive impactful solutions. Collaborating with a multi-disciplinary team, you will contribute to the advancement of scientific knowledge and the firm's reputation in the AI domain. This is a unique opportunity to shape the future of AI applications in therapeutics and materials, making a significant impact in a forward-thinking environment.

Qualifications

• 2+ years of experience in statistics, mathematics, or computer science.
• Proven experience applying machine learning techniques to solve business problems.
• Ability to translate technical methods to non-technical stakeholders.

Responsibilities

• Develop AI and machine learning models and pipelines.
• Support client discussions and prototype development.
• Ensure statistical validity and translate results for senior stakeholders.

Skills

Statistical Analysis

Machine Learning

Python Programming

Data Visualization

Causality

Bayesian Statistics

Deep Learning

Education

Master’s or PhD in Statistics, Mathematics, or Computer Science

Tools

pandas

numpy

matplotlib

scikit-learn

pymc

pytorch

TensorFlow

GitHub

Job description

Your Growth

You will be working in 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 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