Lead Fulfilment Optimization Manager

About the Role

As a Lead Operations Researchon Targets Fulfillment Optimization Analytics team you will shape the last mile fulfillment and delivery strategy - where speed cost and guest experience collide every day. Youll identify sales growth opportunities define placement and positioning strategy and design decision systems that keep the guest at the centerwhile 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. Youll 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-endfrom 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 Youll Work On (Examples)

• End-to-end cost-to-serve optimization: Decision framework that minimizes total cost to Targetper orderpick/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 riskto 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 yearsof professional experience with a Bachelors or Mastersin Mathematics Statistics Computer Science Industrial Engineering Operations Research or related field

• 4 yearsof hands-on programming experience in Python(plus SQL; PySpark/R a plus) and building scalable data workflows.

• 4 yearsapplying operations research and advanced analytics(optimization simulation stochastic modeling forecasting causal inference experimentation).

• Demonstrated ability to work with large datasetsand create robust production-ready analytical code.

• Experience applying ML/AI techniqueswhere appropriate (including modern ML frameworks; LLM experience a plus).

• Strong problem framing skillsable 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