About the role
Job Description:
Work with Operations Research team members and IT partners to gather requirements, ask questions, and influence our product and business unit teams to deliver high value/impact features to our products
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Day-to-day:
Works on the mathematical optimization model at the heart of a task assignment model adhering to some complex work rules
Formulate and maintain the MIP
- Translate real business rules into formulation with planners
- Drive the model through the FICO Xpress Java API, generate/inspect .lp files and solver logs.
- Tune the solver; mip-gap, mip-abs-gap, threads, random seed, determinism; reason about branch-and-bound performance and run time.
- Keep the model solver-agnostic behind the LinearExpression / LinearConstraint / Sense / Sign abstraction so it stays testable and portable.
Validate & benchmark solutions
- Analyze data stored in Azure ADLS to debug the model and find issues; mine input/output. datasets and solver logs to surface data-quality and infeasibility problems, derive insights, diagnose root causes, and resolve modeling bugs (e.g., bad defaults, constraint conflicts).
- Build and maintain the Streamlit dashboard (Python) that visualizes optimizer solutions - code and ship changes directly.
- Work fluently with AI coding agents (Claude / Copilot / similar) to prototype and change models, scripts, and the dashboard fast without lowering the bar on correctness or code quality.
- Partner closely as a team player with the Java engineer to product ionize.
Description of t he role:
A hands-on optimization scientist who has shipped MIP models into production, not just notebooks. They can sit with a maintenance planner, extract fuzzy operational rules, and turn them into correct, well-scoped linear constraints then make the model solve fast enough to be useful (gap/seed/thread tuning, reformulation, branch-and-bound reasoning). They write maintainable, tested code behind a solver-agnostic abstraction, validate feasibility rigorously, and communicate trade-offs (objective design, soft vs hard constraints, default-value handling) to both engineers and business stakeholders. Genuine FICO Xpress (or Gurobi/CPLEX) depth is the rare, critical differentiator. They hold high standards for their code, are a collaborative team player (open to ideas and feedback, no solo work), use AI coding agents (Claude / Copilot) to iterate quickly, comfortably build/change a Streamlit dashboard, and are a fast learner. Perform analysis and derive insights from data.
How you will fit into team:
They define the variables, constraints, and objective behind the abstraction layer and own solution quality and solver performance, while the Java engineer owns the surrounding service, data integrations, and deployment. Together they form the core "Java/Spring engineering + MIP optimization" pairing that makes this a specialized OR product team rather than a standard microservice squad. They also interface directly with Maintenance Planners to validate that optimizer output beats the manual baseline.
This role will be doing software development for optimization model. Develop code to translate model business requirement into a mathematical model / Mathematical programming model. Develop and interpret mathematical model.
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