Oliver Wyman - Lead Data Scientist

at  Oliver Wyman Group

YOUR EXPERIENCE & QUALIFICATIONS

You are a well-rounded technologist who brings a wealth of real-world experience and:

• 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, which 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 enabling solutions to be deployed across environments quickly and repeatably
• Demonstrating comfort and poise in environments where large projects are time-boxed, and therefore consequential design decisions may need to be made and acted upon rapidly
• Demonstrated fluency in modern programming languages for data science (i.e. at least Python, other expertise welcome), covering the full ML lifecycle (e.g. data storage, feature engineering, model persistence, model inference, and observability) using open-source libraries, including:
• Knowledge of one or more machine learning frameworks, including but not limited to: Scikit-Learn, TensorFlow, PyTorch, MxNet, ONNX, etc.
• Familiarity with the architecture, performance characteristics and limitations of modern storage and computational frameworks, with cloud-first considerations for Azure and AWS particularly welcome
• A history of compelling side projects or contributions to the Open-Source community is valued but not required
• Solid theoretical grounding in the mathematical core of the major ideas in data science:
• Deep understanding of a class of modelling or analytical techniques (e.g. Bayesian modeling, time-series forecasting, etc.)
• Fluency in the mathematical principles and generalizations of data science – e.g., Statistics, Linear Algebra and Vector Calculus
• Experience presenting at high-impact data science conferences and solid connections to the data science community (e.g., via meetups, continuing relationships with academics, etc.) is highly valued
• Interest/background in Financial Services, and capital markets in particular, Healthcare and Life Sciences, Consumer, Retail, Energy, or Transportation industries

HOW TO APPLY

If you like what you’ve read, we’d love to hear from you! You can find this and other roles and submit your CV at https://careers.marshmclennan.com/global/en/oliver-wyman-search. And please include a short note introducing yourself and what you’re looking for.
The application process will include both technical testing and team fit interviews.
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Our clients drive our projects – and no two OW Digital projects are the same. You’ll be working with varied and diverse teams to deliver unique and unprecedented products across industries. As a Lead Data Scientist, you are primarily responsible for managing technical projects, including data engineering, model selection and design, and infrastructure deployment in both internal and client environments. We want and expect our people to develop deep expertise in a particular industry (financial services, health and life sciences, etc.), but you should be comfortable developing methods and selecting approaches based on a combination of first principles thinking, curiosity, and your pre-built foundations of software engineering and development. As a Data Scientist, you will work alongside Oliver Wyman partners in the Digital and other practice groups, engage directly with clients to understand their business challenges, and craft appropriate solutions to be delivered through collaboration with other OW Digital specialists and consultants.

Your responsibilities will include:

• 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