Sr. Lead Data Scientist or Principal Data Scientist

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

• 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

• 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 nonlinear methods) and unsupervised learning (clustering, matrix factorization methods, outlier detection, etc.)
• Leveraging cloud‑based infrastructure‑as‑code (Cloud Formation, Bicep, Terraform, etc.) to minimize deployment toil and enable solutions to be deployed across environments quickly and repeatably
• Demonstrating comfort and poise in environments where large projects are time‑boxed, requiring rapid, consequential design decisions
• Fluency in modern programming languages for data science (e.g., Python) covering the full ML lifecycle (data storage, feature engineering, model persistence, model inference, and observability) using open‑source libraries
• Knowledge of one or more machine learning frameworks (Scikit‑Learn, Tensor Flow, 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 major data science ideas (e.g., Bayesian modelling, 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.)
• Interest/background in Financial Services, and capital markets in particular;
  Healthcare and Life Sciences;
  Consumer;
  Retail;
  Energy; or Transportation industries

• 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 transparent 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

• Base salary range: $150,000 to $195,000. Performance‑based incentives may be available.
• Competitive total rewards package including health and welfare benefits, tuition assistance, 401(k) savings, retirement programs, and employee assistance programs.

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_