Lead Data Scientist

Location: Vaughan, Canada

Department: Development

Experience: 10+ Years

• Own the technical architecture of large-scale optimization systems built on MILP/MIP, constraint programming, and heuristic/metaheuristic solvers (e.g., Gurobi, CPLEX, OR-Tools).
• Design and scale routing, scheduling, resource allocation, and network optimization models that account for real-world constraints such as capacity, time windows, territory or zoning restrictions, and service-level commitments.
• Set modeling standards, solver performance benchmarks, and reusable optimization frameworks used across the data science team.
• Integrate ML-based forecasting (demand, consumption, ETAs, anomalies) with the optimization engine to move decision-making from reactive to prescriptive.

• Define the architecture for deploying optimization and ML solutions in production on cloud/lakehouse platforms (e.g., Azure, Databricks), including validation, monitoring, and rollback strategy.
• Lead design reviews and set the bar for model governance, testing, and code quality across the team.
• Partner with Data Engineering to productionize solvers and pipelines at the throughput required for large-scale, recurring optimization runs.

• Translate operational priorities from business and operations leaders into a prioritized optimization roadmap.
• Present model trade-offs, assumptions, and business impact clearly to executive and non-technical audiences.
• Hire, mentor, and grow a team of data scientists and optimization engineers; establish career development, code review, and model review practices.

• 10+ years of experience in Data Science, Operations Research, Applied Mathematics, Industrial Engineering, or an equivalent quantitative field, including 3+ years leading or mentoring a team.
• A proven track record shipping production MILP/MIP or VRP systems that solve real routing, scheduling, resource allocation, or network optimization problems at scale — not only academic or proof-of-concept work.
• Expert-level Python and SQL, with hands-on experience using at least one commercial-grade solver (Gurobi, CPLEX) and/or OR-Tools.
• Experience combining ML forecasting with optimization (e.g., demand or consumption forecasting feeding a scheduling or planning engine).
• Experience deploying and operating models on cloud/lakehouse platforms (Azure, Databricks, or equivalent).
• Strong written and verbal communication; comfortable presenting technical trade-offs to executive stakeholders.

• Master's or PhD in Operations Research, Applied Mathematics, Industrial Engineering, Data Science, or a related field.
• Experience applying optimization in supply chain, logistics, transportation, manufacturing, retail, or field-operations settings.
• Familiarity with simulation, digital twins, reinforcement learning, or prescriptive analytics.
• Experience with Spark or distributed computing for large-scale data processing.
• Exposure to MLOps and model governance frameworks.

• Work directly with Fortune 500 executives and digital transformation leaders.
• Be part of a fast-growing, AI-native software engineering firm redefining global delivery through our GCC-as-a-Service model.
• Collaborate with global teams in Canada, the U.S., and India on cutting-edge cloud and AI initiatives.
• Competitive compensation, performance incentives, and a culture that values ownership, agility, and innovation.