This role pays more than
66%
of similar roles. Most pay
$186,437–$278,000
— the shaded band above.
At the midpoint, this role pays about
$262k
versus about
$232k
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
58 open roles
on FindRole.
Listed pay typically runs
$98,000–$176,000
across 58 roles with salary data.
The Director of Data Sciences - Supply Chain Network Planning and Design leads a team of data scientists and operations research practitioners to shape long-term supply chain architecture and investment roadmaps. This role involves building analytical models, decision-support systems, optimization models, simulations, digital twins, forecasting, and capacity models to evaluate network investments. The position requires expertise in mathematical optimization, machine learning, statistical modeling, and network planning within large-scale environments like retail or logistics. Candidates must be proficient in Python, Scala, Java, SQL, or R, with deep knowledge of optimization theory, dynamic programming, linear algebra, probability, and statistics. You will translate complex technical work into strategic recommendations for senior leadership while partnering with engineering teams to develop scalable tools using cloud computing and modern solvers to solve problems regarding inventory placement, product flow, and transportation.
Lead and develop a high-performing team of data scientists and operations research practitioners.
Translate strategic business questions into quantitative decision frameworks for network planning and design.
Develop optimization models, simulations, digital twins, and forecasting tools to shape long-term supply chain architecture.
Apply advanced analytics to decisions regarding network footprint, capacity planning, inventory placement, and transportation.
Partner with Engineering and Product teams to transform analytical models into scalable decision-support tools.
Establish practices for model governance, reproducibility, testing, documentation, and auditability.
Communicate complex technical recommendations and trade-offs to influence senior executive leadership.
Champion the use of machine learning, cloud computing, and modern optimization solvers to improve decision quality.
What we're looking for
PhD or MS in Operations Research, Industrial Engineering, Computer Science, Mathematics, Statistics, Physics, or relevant industry experience.
10 plus years of experience leading and developing teams of applied data scientists and operations research scientists.
5 plus years of experience in mathematical optimization, simulation, forecasting, machine learning, statistical modeling, and network modeling/planning.
Extensive experience in supply chain, logistics, retail, transportation, manufacturing, or other large-scale network environments (preferred).
High proficiency in Python, Scala, Java, SQL, or R programming.
Demonstrated expertise in optimization theory, dynamic programming, linear algebra, probability, and statistics.
Experience in statistical inference and deploying machine learning models at scale.
Ability to translate complex analytical work into strategic recommendations for senior leaders across technical and executive audiences.
Machine Learning
Python
SQL
Java
Spark
Scala
R
scikit learn
TensorFlow
Data Visualization
Optimization Models
Data Governance
Statistics
Econometrics
Operations Research
WCAG) 2.2 AA
Python
SQL
Machine Learning
Natural Language Processing
ETL
ELT
Data Modeling
Oracle Analytics Cloud
Tableau
Power BI
Oracle Fusion Cloud Applications
Predictive Analytics
Cloud Computing
Distributed Systems
Data Pipelines