Sr Operations Research Scientist - Supply Chain Network Optimization (Python, ML, Simulation)

Job Id: R0000437007

The pay range is $98,000.00 - $176,000.00

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 US AS A SR OPERATION RESEARCH SCIENTIST – SUPPLY CHAIN NETWORK 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.

About this opportunity:

Do you want to shape the future Supply Chain and Logistics strategy for a fortune 50 company and one of America’s leading Retailer? Do you want to optimize Target’s Global Supply Chain end to end, from sourcing to delivery of packages to guest? Are you excited at the prospect of analyzing large data sets and developing complex algorithms, simulation and optimization models to solve real world problems? Then this may be the ideal career opportunity for you!

The Supply Chain Network Optimization (SCNO) team is a global team integrated in the Operations Planning and Network steering within Global Supply Chain and Logistics. We are at the forefront of defining and enabling an efficient, reliable and best in class supply chain. This team uses a wide variety of engineering and advanced applied mathematical techniques (ML, AI, OR, MILP, DES,) techniques to study and solve niche problems in supply chain across the entire value chain (Purchasing, Transportation, Multi Echelon Inventory, Last-Mile and Process Optimization) to enable the best guest experience and profitable growth for Target.

As a Sr Operation Research Scientist in SCNO, you will be studying the complex problems in supply chain, understanding the business well and develop cutting-edge solutions. The role requires you to have strong working experience with large data sets, strong programming skills (Python/R) and foundational understanding of probability theory, machine learning and Monte Carlo simulations. Extensive knowledge of statistics, operations research and discrete event simulation is ideal as well.

As a Senior Operations Research Scientist you will:

• Know your business and understand supply chain operational metrics and cost of end to end operations of supply chain.
• Analyze large amounts of supply chain data at the most granular level and develop key metrics to evaluate the performance and optimization opportunities for supply chain across sales, transportation, operations and inventory.
• Perform root cause analysis to understand key drivers related to operational performance and variance in the projected outcomes.
• Utilize operations research methodologies (linear programming, stochastic processes, simulation) to solve complex business problems.
• Collaborate with cross-functional teams including software engineers, applied data scientists and business stakeholders to gather inputs assumptions and understand business process along the way.
• Interpret results from the models and provide insights to the leadership team.
• Develop a clear inferences with data to influence decision making with Sr leaders.

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

About You:

• PhD or MS in Operations Research, Industrial Engineering, Computer Science, Data Science, Applied Mathematics, Physics or equivalent work experience
• 3+ years of industry experience in supply chain optimization, simulation and probability statistics or equivalent domain experience
• Demonstrated programming experience using Python or R as well as advanced SQL querying skillset
• Proven experience in data pipeline building in the Hadoop ecosystem (PySpark)
• Extensive knowledge of supply chain concepts such as forecasting, inventory planning, optimization and logistics
• Knowledge of statistics and data mining techniques/tools to analyze and derive insights from historical sales data and simulation or experiment results
• Deep passion for problem solving, empirical research and continuous improvement
• Excellent communication skill with the ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives
• Self-driven and results oriented - able to meet tight timelines
• Motivated, team player with ability to collaborate effectively across global team

This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.

Benefits Eligibility

Americans with Disabilities Act (ADA)

In compliance with state and federal laws, Target will make reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to candidate.accommodations@HRHelp.Target.com. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed through this channel.