Lead Data Scientist

Lead Data Scientist Duration: 6-month contract Location: Toronto, ON About the Role We are seeking an experienced Lead Data Scientist to drive the technical strategy and hands-on development of large-scale optimization and machine learning solutions that support complex operational decision-making. In this role, you will lead the design and development of optimization models, including Mixed-Integer Linear Programming (MILP), Vehicle Routing Problems (VRP), constraint programming, and machine learning solutions. You will transform complex, high-volume operational challenges into scalable, production-ready decision systems. This is a player-coach position where you will establish technical direction and mentor a team of data scientists while remaining hands-on with solver design, model architecture, and production deployment. You will work closely with business, engineering, and executive stakeholders to translate operational challenges into optimization solutions that deliver measurable improvements in cost, efficiency, and service quality. What You'll Do Optimization & Model Architecture Lead the technical architecture of large-scale optimization systems using MILP/MIP, constraint programming, and heuristic or metaheuristic solvers such as Gurobi, CPLEX, and OR-Tools. Design and scale routing, scheduling, resource allocation, and network optimization models that account for real-world constraints, including capacity, time windows, territory restrictions, and service-level commitments. Establish modelling standards, solver performance benchmarks, and reusable optimization frameworks across the data science team. Integrate machine learning-based forecasting, including demand, consumption, estimated arrival times, and anomaly detection, with optimization engines to support proactive and data-driven decision-making. Technical Leadership & Delivery Define the architecture for deploying optimization and machine learning solutions in production cloud and data platform environments, including model validation, monitoring, and rollback strategies. Lead technical design reviews and establish standards for model governance, testing, and code quality. Partner with Data Engineering teams to productionize optimization models and data pipelines capable of supporting large-scale, recurring optimization processes. Stakeholder Partnership & Team Leadership Translate business and operational priorities into a structured and prioritized optimization roadmap. Communicate model trade-offs, assumptions, and business impact clearly to executive and non-technical stakeholders. Mentor and develop data scientists and optimization engineers, establishing best practices for career development, code reviews, and model reviews. Provide technical guidance and foster collaboration across multidisciplinary teams. What You'll Need Required Qualifications 10+ years of experience in Data Science, Operations Research, Applied Mathematics, Industrial Engineering, or a related quantitative field. 3+ years of experience leading, mentoring, or providing technical direction to a team. Proven experience delivering production MILP/MIP or VRP solutions addressing real-world routing, scheduling, resource allocation, or network optimization challenges at scale. Expert-level Python and SQL skills, with hands-on experience using at least one commercial-grade optimization solver such as Gurobi, CPLEX, or OR-Tools. Experience integrating machine learning forecasting models with optimization solutions, such as demand or consumption forecasting feeding scheduling or planning engines. Experience deploying and operating models within cloud and data platform environments such as Azure, Databricks, or equivalent technologies. Strong written and verbal communication skills, with the ability to present technical concepts and trade-offs to executive stakeholders. Preferred Qualifications Master's or PhD in Operations Research, Applied Mathematics, Industrial Engineering, Data Science, or a related field. Experience applying optimization within supply chain, logistics, transportation, manufacturing, retail, or field operations. 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. Tools & Technologies Python | SQL | Gurobi | CPLEX | OR-Tools | Azure | Databricks | MILP/MIP | VRP | Spark | MLOps Why Consider This Opportunity? Take ownership of the technical direction and development of large-scale optimization and machine learning solutions. Work on complex operational challenges with measurable business impact. Collaborate with cross-functional technical teams and senior business stakeholders. Provide technical leadership and mentorship while remaining hands-on with solution development and delivery.