Oliver Wyman - Sr. Lead Data Scientist or Principal Data Scientist

About the role

Oliver Wyman is a global leader in management consulting with offices in 50+ cities across 30 countries. Oliver Wyman Digital partners with clients to deliver breakthrough outcomes for their toughest digital challenges, blending digital technology with deep industry expertise to tackle disruption, build capabilities, and embed digital transformation. The team modernizes technology, harnesses value from data and analytics, and builds resilience for future risks while collaborating closely with client leaders, employees, stakeholders, and customers.

Responsibilities

• Exploring data, building models, and evaluating solution performance to resolve core business problems
• Explaining, refining, and collaborating with stakeholders through the journey of model building
• Keeping up with your domain’s state of the art & developing familiarity with emerging modelling and data engineering methodologies
• Advocating application of best practices in modelling, code hygiene and data engineering
• Leading the development of proprietary statistical techniques, algorithms or analytical tools on projects and asset development
• Working with Partners and Principals to shape proposals that leverage our data science and engineering capabilities

Requirements

• Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or other quantitative and computational science
• Compelling track record of designing and deploying large-scale technical solutions that deliver tangible, ongoing value, including:
  • Building and deploying robust, complex production systems that implement modern data science methods at scale, including supervised learning (regression and classification with linear and non‑linear methods) and unsupervised learning (clustering, matrix factorization methods, outlier detection, etc.)
  • Leveraging cloud‑based infrastructure‑as‑code (CloudFormation, Bicep, Terraform, etc.) to minimize deployment toil and enable solutions to be deployed across environments quickly and repeatably
  • Demonstrating comfort and poise in time‑boxed environments where consequential design decisions must be made and acted upon rapidly

Qualifications

• Demonstrated fluency in modern programming languages for data science (at least Python; other expertise welcome), covering the full ML lifecycle (data storage, feature engineering, model persistence, model inference, observability) using open‑source libraries, including knowledge of one or more machine learning frameworks such as Scikit‑Learn, TensorFlow, PyTorch, Mx