Sr Data Scientist - Supply Chain Optimization (Middle Mile)

The pay range is $98,000.00 - $211,000.00 Pay is based on several factors which vary based on position.These include labor markets and in some instancesmay include

, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health

and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves.Other

for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation.Find competitive

from financial and

to well-being and beyond at https://corporate.target.com/careers/

. JOIN TARGET AS SR DATA SCIENTIST - SUPPLY CHAIN OPTIMIZATION

: Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here. A role withTargetData Sciencesmeans the chance to help develop and managestate of the art predictiveand prescriptive algorithmsthatuse data to automateand optimize decisions at scale. The Target Data Science - Global Supply Chain and Logistics team leverages operations research, simulation and machine learning to help Target makes intelligent, end-to-end supply chain business decisions. This involves writing high-performant, scalable Python code as well as designing analytical solutions to extract insights and to prescribe best courses of action. You'll work in an environment that provides the freedom and agility of a startup - with the security and vast resources of a large established company with continuous learning opportunities provided. In this role at Target as a Sr Data Scientist - Supply Chain you will: • Work closely with fellow data scientists to review and iterate on optimization and machine learning algorithms and develop state of the art mathematical models • Collaborate with engineers to design and deploy algorithms in production at scale. Our models run many times a day for millions of combinations of stock keeping units and distribution networks • Work with business partners to learn about the retail business and processes in play • Ideate and resolve tradeoffs between model granularity and features as well as performance, reliability, and usability of our codebase. Improve engineering standards, tooling, and processes