Oliver Wyman

2 months ago

Montréal QC HV A, Canada Oliver Wyman Group Full time

**Boston, Chicago, New York, Dallas, Toronto, Montreal***

**Lead Data Scientist**

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**WHO WE ARE**

**_ Oliver Wyman _**is a global leader in management consulting. With offices in 50+ cities across 30 countries, Oliver Wyman combines deep industry knowledge with specialized expertise in strategy, finance, operations, technology, risk management, and organizational transformation. Our 4000+ professionals help clients optimize their business, improve their IT, operations, and risk profile, and accelerate their organizational performance to seize the most attractive opportunities. Our professionals see what others don't, challenge conventional thinking, and consistently deliver innovative, customized solutions. As a result, we have a tangible impact on clients’ top and bottom lines. Our clients are the CEOs and executive teams of the top Global 1000 companies.

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**PRACTICE OVERVIEW**

At **_Oliver Wyman Digital_** we partner 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.

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**THE ROLE AND RESPONSIBILITIES**

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
- 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
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**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 sto

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