Operation research engineer

Syngenta Group•Malta, IL

4d•$106,000 - $145,000•Remote

About The Position

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.

Requirements

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.
Strong analytical mindset with the ability to communicate complex concepts clearly to technical and non-technical audiences.

Nice To Haves

Experience in Manufacturing, Supply Chain, or Logistics environments.
Knowledge of combinatorial optimization techniques.
Familiarity with modern development practices and tooling (Git/GitHub, code reviews, testing).
Experience using AI-assisted coding tools (e.g., GitHub Copilot).
Experience building lightweight decision-support interfaces and visualizations (e.g., Streamlit or similar) to support user adoption.
Ability to operate effectively in a matrix organization by building strong partnerships and influencing stakeholders at multiple levels without direct authority.
Ability to generate strategic insights from complex analyses and communicate them clearly to drive measurable improvements in operational efficiency and effectiveness.

Responsibilities

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.