Operations Research and Optimization Specialist
About Us
At PeopleLC, we are the architects behind the teams building the future at our Client partner. We design, deploy, and manage the highly sophisticated screening, assessment ecosystem, and career management that evaluates every single engineer, candidates, and leaders applying to work at the Client place. We don't just look for professionals; we use advanced behavioral, technical, and psychological data models to identify the exact quadrant of engineering and workforce excellence required for our Client's success. PeopleLC is a premier career management, HMTR, AI resources, and workforce solutions firm. We act as the strategic architecture partner for high-innovation companies
Description
Our Client are building the future of work, innovating on our customers ’behalf across industries including healthcare, financial services, transportation and supply chain, and retail. We are crafting a unique workplace culture that combines data-driven rigor, relentless customer obsession, and high ownership with innovation and employee-centric creativity. We work hard, iterate fast, and care deeply about building a supportive, elite team.
Description
Our Client is looking for an Operations Research / Optimization Specialist to turn complex operational decisions into models that run in production. You will join the team behind SignalOS, our operational decisioning platform, and design the optimization engines that help customers price, plan, schedule, and allocate under real-world constraints and uncertainty. Our problems span freight pricing, supply chain control towers, workforce and hiring decisions, and healthcare operations. You will own problems end to end: framing the decision with customers, formulating and solving the model, shipping it into the platform, and proving the business impact. This role suits someone who wants their models to change how large organizations operate, not just sit in a notebook.
Key Job Responsibilities
Partner with customers, product, and engineering to translate ambiguous business problems into well-defined optimization and decision models.
Formulate and solve linear, mixed-integer, constraint, network, and stochastic optimization problems using solvers such as Gurobi, CPLEX, OR-Tools, or HiGHS.
Build simulation and scenario models to test policies, quantify trade-offs, and stress-test recommendations before they reach customers.
Combine optimization with forecasting, machine learning, and reinforcement learning signals inside the SignalOS pipeline to produce explainable, actionable recommendations.
Write production-quality Python, working with engineers to deploy models as scalable, monitored services with the right latency and reliability. Define success metrics, run experiments and backtests, and report measurable outcomes such as cost, revenue, service
level, and utilization.
Create reusable modeling components and patterns that let us move quickly across industries.
Explain model logic and trade-offs clearly to executives and frontline operators alike.
Your typical day
You might start the morning in a working session with a rail customer’s pricing team, mapping their commercial rules into constraints. After lunch you tune a MIP formulation that is timing out on a larger instance, then pair with an engineer to wire the improved model into a SignalOS pipeline stage. You end the day reviewing backtest results and drafting a short readout on margin impact for the account lead.
Note on your contact details and requirement
PeopleLC reserve the right to share your contact details and profile with third parties for job matching, business requirements, and marketing or sales initiatives.
Basic Qualifications
Master’s degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or a related quantitative field.
3+ years of experience building and deploying optimization models to solve business problems.
Strong command of mathematical programming (LP, MIP, constraint programming) and hands-on experience with at least one commercial or open-source solver. Proficiency in Python and modeling libraries such as Pyomo, PuLP, gurobipy, or OR-Tools.
Experience working with large, messy datasets using SQL and Python data tooling.
Ability to communicate technical results to non-technical stakeholders.
Preferred Qualifications - optional
PhD in Operations Research or a closely related field.
Experience with stochastic or robust optimization, decomposition methods, heuristics, or metaheuristics.
Experience combining optimization with machine learning or reinforcement learning.
Domain experience in pricing, revenue management, transportation, supply chain, workforce planning, healthcare, or financial services.
Experience deploying models on cloud platforms (AWS, Azure, or GCP) with containers and CI/CD.
Track record of customer-facing work in a consulting, startup, or product environment.
Our Client is an equal opportunity employer. We value diversity and do not discriminate on the basis of race, religion, color, national origin, sex, gender identity, sexual orientation, age, marital status, veteran status, disability status, or any other legally