Lead Data Scientist - Last Mile Supply Chain Optimization

The pay range is $128,000.00 - $277,000.00

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 A LEAD DATA SCIENTIST – LAST MILE OPS DESIGN

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.

Join Target’s Last Mile and Operations Design team where your goal will be to accelerate omni-channel experimentation to foster a culture of continuous innovation at Target. We believe that continuous innovation is critical to delivering and exceeding our brand promise of “Expect More Pay Less” to Target’s guests irrespective of their preferred channel of engagement and commerce.  You will enable continuous innovation and drive business performance in our Target Last Mile Delivery space.

A Lead Data Scientist role within Last Mile and Ops Design is a chance to help lead and define our continued development of our in-house strength for getting the maximum value from Target’s scale and data assets in service of improving the guest and carrier/courier experience.  We see data as a product and an opportunity to experiment and refine our approach to guest experiences.  Together, we positively impact changes to various initiatives, processes and interdependencies that require exceptional data collection, measurement, and timely industry leading analysis—ultimately leading to quicker and more meaningful decision-making.  This is a role for someone passionate about data, feature experimentation, and impacting business strategy.

As a Lead Data Scientist, you will:

• Serve as an advocate of our data-driven culture across Product, Business Ops and Engineering to help them utilize insights that’ll direct their efforts towards initiatives with highest impacts
• You have a strong knowledge of supply chain optimization including the impact of inventory placement on, transportation, sourcing, distribution, fulfillment, and planning
• Execute solutions to business problems using data analysis, data mining, forecasting, optimization tools, and machine learning techniques and statistics
• Formulate precise problem statements, synthesize analysis and results in compelling storylines and communicate to key stakeholders. You should have the ability to tell a story around the problem, the solution and the impact and value of implementing a solution
• Analyze and explore data in our ecosystem, work with the product manager to design in-product experiments, analyze the results, and provide strategic recommendations.
• Partner closely with our Product Owners and/or business partners to identify gaps in product offering by conducting comprehensive data analysis, and accelerating the analytics maturity (predictive and prescriptive) in key product areas
• Define data to be collected for measuring the success of new product features or business strategies, define KPIs, and work with engineering and across functional teams on tracking and instrumentation of data
• Strive for query optimization problems on large data sets that wi