About the Role
As a Lead Operations Research on Target’s Fulfillment Optimization Analytics team, you will shape the last mile fulfillment and delivery strategy - where speed, cost, and guest experience collide every day. You’ll identify sales growth opportunities, define placement and positioning strategy, and design decision systems that keep the guest at the center while improving profitability and reliability.
This is a high-impact role working across business, product, and engineering teams to solve complex problems spanning network strategy, delivery promise, service design, order allocation, and dynamic fulfillment decisions. You’ll blend operations research, simulation, and data science to deliver scalable solutions that measurably improve last mile performance.
Key Responsibilities
- Lead last mile strategy and optimization: Define and optimize delivery strategy (e.g., same-day/next-day options, service levels, coverage, and promise/allocation logic) to drive guest value and business results.
- Identify growth opportunities: Use data and experimentation to uncover ways to increase sales via improved delivery experience, coverage expansion, and better placement/positioning decisions.
- Build decision models at scale: Develop optimization, simulation, and forecasting models to solve problems such as delivery zone design, capacity allocation, carrier mix, routing tradeoffs, and service tier decisions.
- Hypothesis-to-scale execution: Formulate hypotheses, build proof-of-concepts, measure impact with clear success metrics, and scale solutions based on learnings and iteration
- Guest-centric tradeoff design: Quantify tradeoffs between speed, availability, cost-to-serve, and reliability; recommend clear, actionable strategies grounded in guest outcomes.
- Run scenario planning and network what-ifs: Execute complex simulations and scenario analyses to evaluate policy changes and operational levers under uncertainty.
- Translate ambiguity into execution: Frame vague business questions into well-defined analytical problems, propose solution approaches, and deliver end-to-end—from model to recommendation to implementation plan.
- Drive cross-functional delivery: Build project charters, milestones, and success metrics; align stakeholders; remove blockers; and ensure high-quality delivery with measurable impact.
- Communicate with influence: Create clear narratives, decision memos, and executive-ready readouts; explain modeling assumptions and risks in a way that drives confident decisions.
- Raise the OR bar: Mentor others, review technical work, elevate standards for modeling, validation, and operationalization.
What You’ll Work On (Examples)
- End-to-end cost-to-serve optimization: Decision framework that minimizes total cost to Target per order—pick/pack labour + store/FC handling + delivery/carrier costs + markdowns/cancellations, while meeting promise, capacity constraints, and service-level targets.
- Order allocation to minimize total cost: Optimize the decision of which node fulfils each order (store vs FC ) by balancing last-mile delivery cost, inventory availability, store workload, and substitution/cancellation risk to reduce overall cost while protecting conversion and guest experience.
- Capacity planning, dynamic cutoffs, and operational policies
- Scenario planning for new delivery partnerships, cost structures, and service tiers
About You (Qualifications)
- 8+ years of professional experience with a Bachelor’s or Master’s in Mathematics, Statistics, Computer Science, Industrial Engineering, Operations Research, or related field
- 4+ years of hands-on programming experience in Python (plus SQL; PySpark/R a plus) and building scalable data workflows.
- 4+ years applying operations research and advanced analytics (optimization, simulation, stochastic modeling, forecasting, causal inference, experimentation).
- Demonstrated ability to work with large datasets and create robust, production-ready analytical code.
- Experience applying ML/AI techniques where appropriate (including modern ML frameworks; LLM experience a plus).
- Strong problem framing skills—able to turn complex, cross-functional ambiguity into solvable models and measurable outcomes.
- Excellent communication and stakeholder management skills; comfortable influencing senior leaders with data-backed recommendations.
- Retail, e-commerce, and/or last mile logistics experience strongly preferred.
Core Competencies
- Optimization (LP/MIP, network flow, heuristics/metaheuristics)
- Simulation and scenario analysis
- Forecasting and uncertainty modeling
- Experimentation and measurement (A/B tests, causal inference)
- Business strategy + analytical execution with guest-first thinking