Lead Data Scientist, Supply Chain Network Planning Design

Target

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Minneapolis, MNSunnyvale, CA
Salary
$132,000–$286,000 / yr
Employment
Full-time
Posted
3 days ago
Freshness
Confirmed live yesterday
Closes
Nov 20, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $202k
This role $209k
$114k $304k
below market most similar roles pay here above market

This role pays more than 50% of similar roles. Most pay $162,000–$241,750 — the blue band above. At the midpoint, this role pays about $209k versus about $202k for comparable roles.

Based on 240 similar postings.

Employer

About Target

Target Corporation is a large-format general merchandise and grocery retailer offering a wide assortment of everyday essentials, apparel, home goods, and electronics through stores and online. Industry: General Merchandise Retail

Target currently has 72 open roles on FindRole.

Listed pay typically runs $98,000–$176,000 across 55 roles with salary data.

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View all roles at Target

At a glance

TL;DR · Lead Data Scientist, Supply Chain Network Planning Design

The Lead Data Scientist - Supply Chain Network Planning Design joins the Last Mile and Operations Design team to build analytical models and decision-support systems for long-term supply chain architecture. You will develop a portfolio of optimization models, simulations, digital twins, forecasting, and capacity models to evaluate network investments. Key responsibilities include framing ambiguous supply chain problems, synthesizing insights for executive audiences, and mentoring team members. You will utilize Python, R, Kotlin, or Java to write high-performant code, employing SQL, Hive, and mathematical optimization solvers like Gurobi. The role requires applying operations research, machine learning, and advanced statistical techniques like regression and clustering to solve complex problems regarding network footprint, inventory placement, product flow, and reverse logistics within a large-scale retail supply chain environment.

What you'll do

  • Define and structure ambiguous supply chain problems into clear frameworks for executive decision-making.
  • Develop and maintain a portfolio of optimization models, simulations, digital twins, and forecasting tools.
  • Apply operations research and machine learning to decisions regarding network footprint, capacity, and inventory placement.
  • Translate analytical models into scalable production tools in partnership with Engineering and Product teams.
  • Synthesize complex data insights into compelling narratives and visualizations for senior stakeholders.
  • Mentor and coach data scientists in structured problem solving, systems thinking, and executive communication.
  • Establish best practices for model governance, reproducibility, testing, and documentation.
  • Drive cross-functional alignment on business requirements and strategic implications across Supply Chain, Finance, and Technology.

What we're looking for

  • PhD/MS in Operations Research, Industrial Engineering, Computer Science, Mathematics, Statistics, Physics, or a related quantitative field.
  • 5+ years of experience leading large-scale implementations of supply chain optimization, simulation, and machine learning modeling end to end.
  • Demonstrated proficiency in one or more programming languages including Python, R, Kotlin, or Java.
  • Demonstrated experience writing highly performant code and deploying algorithms in a production environment.
  • Proficiency in predictive/prescriptive algorithms and ML Ops.
  • Experience working with large event-driven distributed systems and multi-threaded applications.
  • Experience in application/software architecture, including definition and process modeling.
  • Experience writing production datasets in SQL/Hive or building internal data tools using a scripting language like Python.
  • Experience working with mathematical optimization libraries or solvers such as Gurobi.
  • Experience implementing advanced statistical techniques like regression, clustering, PCA, and time series forecasting.
  • Experience producing documents and narratives that suggest actionable insights.
  • Excellent communication skills with the ability to tell data-driven stories through visualizations, graphs, and narratives.

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