Oliver Wyman - Senior Data Scientist

Marsh McLennan•Boston, MA

21h•Hybrid

About The Position

Oliver Wyman Digital partners with clients to deliver breakthrough outcomes for their toughest digital challenges. We blend the power of digital technology with deep industry expertise to tackle disruption and create impact. By building strong capabilities and culture, we accelerate and embed digital transformation. Our people co-create and grow customer-focused solutions that win. We modernize technology and harness value from data and analytics. We build resilience so our clients are ready for tomorrow’s risks and can optimize operations for the future. Above all, we work collaboratively with our clients’ leaders, employees, stakeholders, and customers to jointly define, design, and achieve lasting results. As a Data Scientist, you are responsible for supporting technical projects, including data engineering, model selection and design, and infrastructure deployment in both internal and client environments. You will work alongside Oliver Wyman partners in the Quotient within our Performance Transformation practice 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.

Requirements

Technical background in computer science, data science, machine learning, artificial intelligence, statistics, or other quantitative and computational science
Track record of designing and deploying 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.)
Developing Generative AI and Agentic AI solutions, leveraging patterns such as Retrieval Augmented Generation (RAG), multi-agent frameworks and performance evaluation.
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.
Knowledge of one or more of the key generative AI frameworks ackages, including but not limited to: OpenAI, Anthropic, Strands Agents, Langchain, 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
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
An undergraduate or advanced degree from a top academic program
A genuine passion for technology and solving problems
A pragmatic approach to solutioning and delivery
Excellent communication skills, both verbal and written
A clear commitment to creating impactful solutions that solve our clients’ problems
The ability to work fluidly and respectfully with our incredibly talented team
Willingness to travel for targeted client and/or internal stakeholder meetings

Nice To Haves

A history of compelling side projects or contributions to the Open-Source community is valued but not required
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

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