Senior Fellow - Scientific AI (Clinical Biostatistician)

Job Responsibilities

You will work as a member of McKinsey's global scientific AI team to transform the way our clients do research and development in industries where scientific innovation is core to value creation. You will help build and lead McKinsey's forward-looking capabilities to transform clients' innovation engines.

With your specific expertise at the intersection of clinical biostatistics and artificial intelligence / machine learning (i.e., deep learning, causal inference, Bayesian modelling, and foundation models), you will bring distinctive statistical knowledge to complex client Life Sciences R&D challenges through part-time staffing on client studies with a multi-disciplinary team. You will drive proposal processes and participate in client negotiations by identifying end-to-end opportunities to transform Life Sciences R&D and architecting solutions that address specific client needs. You will advance the latest thinking in scientific AI and generate a steady stream of whitepapers, scientific publications and keynote speeches in top scientific and AI conferences and journals. You will also establish an external network, play a lead role in external reputation building, and attract and retain junior colleagues by mentoring them to expand their scientific AI knowledge and impact.

Qualifications

• Must possess an advanced degree – PhD in Biostatistics, Statistics, or a closely related quantitative discipline (or an MD with strong biostatistical training)
• Proven mastery of fundamental concepts in clinical biostatistics, as well as being up-to-date on latest ideas and publications, including:
  • Deep understanding of developing statistical analysis plans (SAPs) in pharma
  • Experience of clinical trial simulation
  • Adaptive clinical trials
  • Bayesian network meta-analysis
  • Platform clinical trials
• Deep understanding of the data sources, AI methods and related analytical technologies, e.g., AI principles, deep learning, machine learning (supervised or unsupervised), causal inference, large language models, foundation models, diffusion models, reinforcement learning, and knowledge graphs
• Ability to develop new-to-world solution architecture blueprints, and decompose the end-state solution into incremental releases to prove feasibility and deliver value
• Track record of innovation at the intersection of AI and clinical trial design / biostatistics
• Track record of recent leadership and engagement in the external ecosystem via publications, conference participation, keynote speeches, and connectedness with academia and/or industry
• Experience managing direct reports, or a complex network of internal & external stakeholders
• Compelling communicator with track record of translating technical methods to non-technical executive stakeholders