Lead Data Scientist - Supply Chain Optimization

The pay range is $128,000.00 - $231,000.00

Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, 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 benefits 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 benefits 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 benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.

JOIN TARGET AS LEAD DATA SCIENTIST – SUPPLY CHAIN OPTIMIZATION

About us:

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 with Target Data Sciences means the chance to help develop and manage state of the art predictive and prescriptive algorithms that use data to automate and optimize decisions at scale.  The Target Data Sciences - Supply Chain Optimization team leverages operations research, simulation, and machine learning to help Target makes intelligent supply chain business decisions.  This involves writing high-performant, scalable 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. Our team aims to be inclusive, collaborative, highly motivated, smart, and humble!

In this role at Target as a Lead Data Scientist – Supply Chain Optimization, 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, deploy and productionize models 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
• Mentor and develop other data scientists and engineers

What we're looking for:

• You have a strong knowledge of supply chain optimization – inventory, transportation, sourcing, distribution, fulfillment and planning
• You have a passion and technical skills for helping Target make data-informed decisions
• You possess strong computer science fundamentals and love rock-solid, well-structured, and efficient code: data structures, algorithms, programming and information retrieval
• You write understandable, testable Python code with an eye towards maintainability
• A passion for empirical research and answering hard questions with large scale data
• You are a strong communicator. Explaining complex technical concepts to the business and other data scientists/engineers is no problem for you
• You understand that simplicity is hard but key to achieving maintainability and to being able to continually deliver value to the business
• You have usability and convenience for business users in mind

Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs.

About you:

• 4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent work experience
• PhD/MS in Operations Research, Industrial Engineering, Computer Science, Mathematics, Statistics, P