Operation research engineer

At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta’s Analytics & Data Sciences Team is seeking an Operations Research Engineer in Malta, IL. This role will drive operational efficiency and decision quality across Seeds Research & Development by designing, building, and scaling algorithmic decision-support solutions.

The role applies Operations Research and Mathematical Optimization to complex end-to-end business processes—translating business objectives and constraints into robust models, integrating them into Syngenta’s digital ecosystem, and enabling measurable improvements in service, cost, speed, and resource utilization.

Accountabilities:

• Design, build, and deploy mathematical optimization models using Linear Programming (LP), Mixed-Integer Programming (MIP), Nonlinear Programming (NLP), Constraint Programming (CP), and hybrid approaches as appropriate.
• Translate business objectives, constraints, and operating policies into robust mathematical formulations with clear assumptions and acceptance criteria.
• Integrate optimization solutions into Syngenta’s digital ecosystem.
• Identify, collect, cleanse, and structure data from multiple sources; define input/output specifications, data quality requirements, and data lineage for optimization models.
• Define and align business KPIs and metrics that drive optimization objectives and enable value tracking.
• Validate models through testing, back-testing, and scenario analysis; run simulations and what-if analyses to assess robustness and quantify trade-offs.
• Lead and manage projects delivering optimization solutions for strategic decisions and day-to-day operations, such as scope, timeline, risks, and stakeholder alignment.
• Collaborate with cross-functional teams and line-of-business users to review current operational systems and processes, identify pain points, and prioritize optimization use cases.
• Communicate clearly on bottlenecks, insights, and recommendations to enable informed decisions by management and operational teams.
• Work in an agile way and adhere to engineering best practices.
• Contribute to capability building by developing technical training materials and curricula to support adoption and awareness of Operations Research solutions.
• PhD in Operations Research, Applied Mathematics, Industrial Engineering, Data Science, Management Science, Computer Science, or a related field.
• 5+ years of applied experience designing, building, and deploying Operations Research / Mathematical Optimization solutions in an industrial context.
• Strong understanding of Operations Research techniques with emphasis on mathematical programming.
• Proficiency in Python for optimization, data processing, and automation; ability to write production-quality, maintainable code.
• Hands-on experience with optimization solvers and modeling tools (e.g., Gurobi, SCIP, FICO Xpress, COPT) and building end-to-end optimization applications.
• Strong problem-framing and structured problem-solving skills: ability to identify complex business problems, assess fit for OR techniques, and deliver scalable decision-support solutions.
• Proven track record delivering implemented business process improvement solutions with measurable impact.
• Ability to identify, access, and translate relevant data into actionable, evidence-based decisions; ensure data quality, consistency, and traceability across sources.