Senior Data Scientist, Global Supply Chain & Logistics

Target; Sunnyvale (CA); Hybrid; USD 118,000 - 211,000; Full time

Benefits offered by this job

Health benefits

401(k)

Disability & life insurance

Paid time off

Job summary

Target is seeking a Sr Data Scientist for Global Supply Chain and Logistics in Sunnyvale. You will develop and deploy state‑of‑the‑art optimization and prescriptive models, collaborating with data scientists and engineers to scale solutions that drive decision‑making across stock, distribution, and transportation networks.

You will translate business needs into data‑driven insights, communicate clearly with partners across time zones, and contribute to maintainable, scalable code.

Qualifications

• PhD or MS in Operations Research, Industrial Engineering, Computer Science, Mathematics, Statistics, Physics, or related quantitative fields.
• 3+ years of experience leading large-scale optimization, simulation, ML and deep learning modeling at scale.
• Proficient in Python, R, Kotlin, or Java; strong production deployment experience.

Responsibilities

• Review and iterate on optimization and ML algorithms with fellow data scientists.
• Collaborate with engineers to deploy algorithms in production at scale.
• Learn the retail business processes and partner with business teams to drive impact.
• Improve engineering standards, tooling, and processes.

Skills

Supply chain optimization

Programming fundamentals

Communication skills

Ambiguity handling

Data-driven storytelling

Team collaboration

Education

PhD or MS in quantitative field

Tools

Python

R

Kotlin

Java

SQL/Hive

Job description

Target is seeking a Sr Data Scientist for Global Supply Chain and Logistics in Sunnyvale. You will develop and deploy state‑of‑the‑art optimization and prescriptive models, collaborating with data scientists and engineers to scale solutions that drive decision‑making across stock, distribution, and transportation networks.

You will translate business needs into data‑driven insights, communicate clearly with partners across time zones, and contribute to maintainable, scalable code.