Career Opportunities

About Oplist

Oplist is building an explainable workforce scheduling platform for complex shift-based organizations, starting with healthcare.

We help hospitals and other 24/7 operations move away from manual scheduling, Excel-based planning, fairness complaints, overtime leakage, and compliance risk; toward mathematical optimization, rule-based scheduling, and better workforce visibility.

The Mission

We are looking for an analytical, detail-oriented, and optimization-minded person to support Oplist’s workforce scheduling product.

This role sits at the intersection of customer implementation, live-case troubleshooting, data analysis, and optimization model literacy.

You will be the person who understands why a schedule came out the way it did, helps customers actually get value from the platform day-to-day, and turns real-world scheduling friction into structured feedback for the product and optimization teams.

This is not a research or model-building role. You won’t be designing new optimization models from scratch. You will need to understand our existing model, constraints, and solver behavior deeply enough to explain, debug, and improve how it’s used in practice.

What You Will Own

Field Case Analysis & Troubleshooting (the core of this role) You will be the first line of investigation for scheduling cases flagged as problematic: infeasible results, unbalanced schedules, unexpected solver behavior reported through our request logs and support channels. You will dig into why a specific case failed or produced a poor result: bad input data, a misconfigured constraint, an edge case the model doesn’t handle well, or a mismatch between what the customer expects and what they configured. You’ll identify the root cause and either resolve it directly or hand it to the optimization team with a clear, structured diagnosis.

Customer & User Onboarding You will work directly with customers to help them configure their scheduling rules correctly, understand how to get reliable results from the platform, and troubleshoot issues as they come up during rollout. You’ll be a steady technical point of contact during implementation. This is a hands-on, ongoing part of the role, not an occasional task.

Data Analysis You will analyze scheduling inputs and outputs; staff lists, shift requirements, availability and historical schedules to catch missing, inconsistent, or unusual data before and after it hits the solver, and to spot patterns across customer cases.

Optimization Model Understanding You will work with mathematical optimization models, decision variables, constraints, objectives, and scheduling rules. You will initially not build them, but you are expected to understand them well enough to reason about feasibility, trade-offs, and why a given input produces a given output.

Product & Model Feedback As you work through onboarding tasks and field cases, you will keep an eye out for missing rules, recurring edge cases, and infeasibility patterns, and bring structured suggestions to the product and optimization teams.

Who You Are

• You genuinely want to work close to customers and real cases — this role lives in implementation and troubleshooting, not in the back office.
• You are analytical, structured, and comfortable working with complex operational problems.
• You have a solid understanding of mathematical modeling and optimization concepts — decision variables, constraints, objectives, feasibility, trade-offs — and can use that understanding to investigate why a solution behaves the way it does, even though you won’t be building the model yourself.
• You’re comfortable being customer-facing: explaining technical scheduling logic clearly to non-technical users, and staying with a case until it’s actually resolved.
• You enjoy detective work — tracing an unexpected result back through data, configuration, and constraints to find the actual cause.
• You are comfortable working in an early-stage startup where the product, model, and customer needs are evolving quickly.

Requirements

• Bachelor’s degree in Industrial Engineering, Operations Research, Mathematics, Statistics, Computer Science, or a related field
• Solid understanding of core optimization concepts: decision variables, constraints, objective functions, feasibility, hard vs. soft constraints
• Comfortable working directly with customers/users to understand their needs and troubleshoot issues
• Strong analytical instincts for investigating unexpected results and tracing them to root cause
• Comfortable working with data, spreadsheets, and structured problem-solving
• Strong attention to detail and ability to work systematically through test cases and case investigations
• Clear written and verbal communication skills in Turkish and English
• Ability to take ownership, learn quickly, and work closely with technical and product teams

Nice to Have

• 1–4 years of experience in operations research, optimization, data analysis, customer implementation, technical support, or a related field
• Experience with Python
• Familiarity with PuLP, Gurobi, CPLEX, OR-Tools, or similar optimization tools
• Experience with Excel, Google Sheets, SQL, or data analysis workflows
• Experience or interest in workforce scheduling, shift planning, healthcare operations, or similar operational problems
• Experience with SaaS customer support, implementation, or technical onboarding processes
• Experience working with real customer data and validating model or system outputs

Why This Role Matters

Oplist’s core value comes from turning complex workforce scheduling rules into explainable, reliable, and usable optimization-based solutions. As we scale implementations, we need someone who lives close to real customer cases. Someone who can look at a confusing or infeasible result and figure out exactly what happened, help the customer get unstuck, and feed what they learn back into a more robust product.

How to Apply

Please send your CV and a cover letter to careers@oplist.io.

In your cover letter, briefly tell us:

• Why this role (customer-facing troubleshooting + optimization) interests you
• Any experience investigating unexpected results, debugging, or solving structured operational problems
• Your comfort level working directly with customers/users

Please make sure your CV is up to date before sending.