Target is hiring a Lead Data Scientist to develop analytical models and decision-support systems that guide long-term supply chain network architecture and investment roadmaps. This hybrid role combines optimization, simulation, forecasting, digital twins, and machine learning to support scalable strategic decision-making.
Role Overview
In this position, you will own the development of capabilities that support Target’s long-term network strategy. You will apply operations research and advanced analytics to decisions spanning network footprint, capacity planning, inventory placement, product flow, automation, transportation, and reverse logistics. You will also partner across Supply Chain, Operations, Finance, Technology, Strategy, and Merchandising to align on assumptions, constraints, and decision contexts.
Key Responsibilities
- Define and structure ambiguous supply chain problems into clear decision frameworks in collaboration with Network Design, Flow, and Capacity Planning teams.
- Synthesize model outcomes into clear narratives for executive audiences and support scaled decision-making.
- Influence senior stakeholders across Supply Chain, Operations, Finance, Technology, Strategy, and Merchandising.
- Own optimization, simulation, forecasting, digital twin, and machine learning capabilities supporting long-term network strategy.
- Ensure models represent real-world constraints, uncertainty, and operating dynamics while remaining scalable and actionable.
- Establish practices for scenario analysis, sensitivity testing, model validation, and decision support.
- Build reusable analytical capabilities to enable faster and more consistent strategic decisions.
- Own, maintain, and continuously enhance strategic planning models to improve accuracy, scalability, and alignment with evolving needs.
- Maintain a cross-functional pulse on customer needs, priorities, and decision context, aligning teams on assumptions, decisions, and requirements.
- Partner with Engineering, Product, and Technology to transform analytical models into scalable decision-support tools and products.
- Advance the use of modern optimization solvers, cloud computing, machine learning, and AI when they improve model performance or decision quality.
- Implement model governance practices, including reproducibility, testing, documentation, and auditability.
- Champion experimentation and emerging methods for network planning improvements.
- Mentor data scientists and analysts in structured problem solving, systems thinking, and executive communication.
- Elevate modeling and analytical standards within the team.
Core responsibilities of this job are described within this job description.
Required Qualifications
- PhD/MS in Operations Research, Industrial Engineering, Computer Science, Mathematics, Statistics, Physics, or a related quantitative field.
- 5+ years of experience leading end-to-end large-scale implementations of supply chain optimization, simulation, and machine learning modeling.
- Demonstrated proficiency in one or more programming languages: 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/production data tools for research or experimentation in a scripting language like Python.
- Experience with mathematical optimization libraries or solvers (for example, Gurobi).
- Passion for solving real-world problems using a data science approach.
- Experience implementing advanced statistical techniques such as regression, clustering, PCA, and forecasting (time series).
- Ability to produce documents and narratives with actionable insights.
- Excellent communication skills, including the ability to clearly tell data-driven stories using appropriate visualizations, graphs, and narratives.
- Self-driven, results oriented, and able to meet tight timelines.
- Collaborative team player with the ability to partner effectively across time zones.
Technologies
- Python, R, Kotlin, Java
- SQL, Hive
- Gurobi
- ML Ops
- Cloud computing, machine learning, AI
Location and Work Model
Minneapolis, MN (Hybrid). Core role work is performed both onsite at the Target HQ location assigned to the role and virtually, depending on daily role, team, and tasks. Work duties cannot be performed outside the country of the primary work location unless otherwise prescribed by Target.
Compensation
USD 132,000 - 286,000 per year
Benefits
- Comprehensive health benefits and programs, which may include medical, vision, dental, and life insurance
- 401(k)
- Employee discount
- Short term disability
- Long term disability
- Paid sick leave
- Paid national holidays
- Paid vacation
- Financial and education and well-being programs (see https://corporate.target.com/careers/benefits)
ADA Statement
- Target will make reasonable accommodations for applicants with disabilities.
- If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to candidate.accommodations@HRHelp.Target.com.
Application Deadline
11/19/2026
