This role pays less than
65%
of similar roles. Most pay
$128,533–$208,835
— the shaded band above.
At the midpoint, this role pays about
$154k
versus about
$169k
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
62 open roles
on FindRole.
Listed pay typically runs
$98,000–$176,000
across 51 roles with salary data.
Sr Data Scientist - Supply Chain Optimization (Middle Mile) joins the Global Supply Chain and Logistics team to develop and manage state of the art predictive and prescriptive algorithms that automate and optimize decisions at scale. The role involves writing high-performant, scalable Python code, designing analytical solutions to extract insights, and building mathematical models including mixed-integer programming, stochastic dynamic programming, and reinforcement learning. You will collaborate with engineers to deploy algorithms into production environments while working with business partners to resolve trade-offs between model granularity and performance. Required skills include proficiency in Python, R, or Java, as well as SQL/Hive for producing production datasets. The role addresses complex supply chain problems involving inventory, transportation, sourcing, distribution, fulfillment, and planning by applying advanced statistical techniques like regression, clustering, PCA, and time series forecasting to solve logistics challenges.
What does a Data Scientist earn in California?
Median $213878 from 88 postings across 32 companies.
Develop state-of-the-art predictive and prescriptive algorithms to automate and optimize supply chain decisions.
Write high-performant, scalable Python code for production environments involving millions of SKU combinations.
Design and implement mathematical models including mixed-integer programming, stochastic dynamic programming, and reinforcement learning.
Collaborate with engineers to deploy optimization and machine learning algorithms at scale.
Build internal data tools and production datasets using SQL or Hive.
Translate complex data into actionable insights through visualizations and narratives for business partners.
Improve engineering standards, tooling, and processes to ensure codebase maintainability and reliability.
What we're looking for
MS or PhD in Industrial Engineering, Operations Research, Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.
3+ years of experience leading large-scale implementations of optimization, simulation, machine learning, and deep learning at scale.
Extensive experience writing highly performant code and deploying algorithms in a production environment.
Expert at formulating and implementing mathematical models including mixed-integer programming, stochastic dynamic programming, and reinforcement learning.
Proficiency in predictive and prescriptive algorithms and advanced statistical techniques like regression, clustering, PCA, and time series forecasting.
Demonstrated experience in one or more programming languages such as Python, R, or Java.
Experience writing production datasets in SQL/Hive or building internal data tools using scripting languages like Python.
Strong communication skills to translate data into actionable insights through visualizations and narratives for business partners.
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