Data Scientist

ABOUT THE PROJECT Our client is seeking an experienced Data Scientist to contribute to a cutting-edge data science initiative focused on advanced optimization and predictive modeling. The role supports complex analytical projects that apply mathematical programming and statistical techniques to solve real-world business problems, while also enabling knowledge transfer to internal teams.This is a fully remote engagement offered as a one-year contract, with potential for extension. ABOUT THE RESPONSIBILITIES In this role, you will design, build, and iterate on advanced optimization and predictive models, translating complex business requirements into scalable, solver-ready mathematical formulations. You will work with large datasets and complex problem spaces, applying strong analytical judgment to balance performance, accuracy, and real-world constraints.You will be expected to operate with a high degree of independence, clearly communicate assumptions and trade-offs, and proactively drive work forward while collaborating with technical and non-technical stakeholders.Key responsibilities include :

• Formulating and implementing optimization models, with a strong focus on mixed-integer linear programming (MILP) and related mathematical programming techniques
• Translating business objectives and constraints into solver-ready formulations and iterating on models to achieve stable, performant solutions
• Working hands-on with optimization solvers and APIs in Python (e.g., Gurobi, CPLEX, OR-Tools, PuLP / COIN-OR), including debugging and refining model behavior
• Developing and applying predictive and statistical models, including Bayesian approaches where appropriate
• Processing, cleaning, and analyzing large datasets using Python and data-wrangling libraries such as Pandas or Polars
• Supporting feature engineering and analytical workflows for large-scale optimization or modeling problems
• Implementing and maintaining data pipelines, including monitoring execution, reviewing logs, and troubleshooting performance issues
• Applying DevOps practices to support reproducibility, deployment, and maintainability of data science solutions
• Working with cloud-based data platforms such as Databricks and Azure Blob Storage
• Clearly communicating assumptions, methodologies, results, and trade-offs to both technical and non-technical audiences
• Producing clear documentation, model artifacts, and analytical readouts to support transparency and knowledge transfer
• Proactively identifying risks, surfacing issues early, and seeking input as needed rather than waiting for scheduled check-ins
• Supporting knowledge transfer and training for internal staff to strengthen organizational data science capabilities

REQUIREMENTS Must-have :

Nice-to-have :

ABOUT THE ROLE

PAY DISCLOSURE The average hourly pay range for this field is as follows :