Boston, Chicago, New York, Dallas, Toronto, MontrealLead Data ScientistThe Role And ResponsibilitiesExploring data, building models, and evaluating solution performance to resolve core business problemsExplaining, refining, and collaborating with stakeholders through the journey of model buildingKeeping up with your domain’s state of the art & developing familiarity with emerging modelling and data engineering methodologiesAdvocating application of best practices in modelling, code hygiene and data engineeringLeading the development of proprietary statistical techniques, algorithms or analytical tools on projects and asset developmentWorking with Partners and Principals to shape proposals that leverage our data science and engineering capabilitiesYour Experience & QualificationsTechnical 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 repeatablyDemonstrating comfort and poise in environments where large projects are time-boxed, and therefore consequential design decisions may need to be made and acted upon rapidlyDemonstrated 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 welcomeA history of compelling side projects or contributions to the Open-Source community is valued but not requiredSolid 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 CalculusExperience 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 valuedInterest/background in Financial Services, and capital markets in particular, Healthcare and Life Sciences, Consumer, Retail, Energy, or Transportation industries.YOUR ATTRIBUTESAn undergraduate or advanced degree from a top academic programA genuine passion for technology and solving problemsA pragmatic approach to solutioning and deliveryExcellent communication skills, both verbal and writtenA clear commitment to creating impactful solutions that solve our clients’ problemsThe ability to work fluidly and respectfully with our incredibly talented teamWillingness to travel for targeted client and/or internal stakeholder meetingsBenefitsBase salary range: $150,000 to $195,000.Performance-based incentives may be available.Competitive total rewards package including health and welfare benefits, tuition assistance, 401K savings, retirement programs, and employee assistance programs.Oliver Wyman is an equal opportunity employer. Our commitment to diversity is genuine, deep, and growing. We are not perfect, but we’re working hard right now to make our teams balanced, representative, and diverse.R_ #J-18808-Ljbffr