Lead Operations Research Scientist - Supply Chain Optimization

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

As a Lead Operations Research Scientist with Target Last Mile Delivery – you’ll develop Optimization models and algorithms that change the game in our last mile delivery operations. You’ll work closely with peer data scientists, ML engineers and business partners to continuously learn more about our business and particularly develop a deep understanding of the various processes we have in play. You’ll also partner with scientists and engineering in our DAART (Delivery and Automated Routing) team to build and productionize solutions for our business needs including design, development, deployment, and maintenance of algorithms and models which will scale and perform.

Our team oversees the development of state-of-the-art mathematical techniques to help solve important problems for Target’s Supply Chain! For example, we’re identifying opportunities to reduce variable costs by improving the routing optimization, network configuration, and scheduling. This contributes to the development, implementation, and maintenance of the machine learning components as it relates to forecasting, scheduling and other initiatives that drive service, community experience, and costs. This also helps create the metrics to quantify improvements to the network costs resulting from the application of these optimization models and tools.

In this role as a Lead Operations Researcher on the Target DAART team, you will drive development and deployment of models/algorithms/tools that solve the optimization problems including:

• Vehicle routing problem with time window constraint considering mixed capacitated fleet
• Network optimization problems at scale
• Forecast last mile delivery demand - finding the optimal schedule to service the demand on time
• Work force optimization problem
• Capacity management throughout the Target last mile delivery network

Requirements:

• MS or PhD in Mathematics, Statistics, Operations Research, Industrial Engineering, Physics, Computer Science or relevant work/research experience
• 3+ years of experience in building Optimization models and algorithms which perform at scale
• Significant expertise in Optimization Theory (deterministic/stochastic), dynamic programming, linear algebra, probability and statistics
• Design and implement complex optimization models for network design, routing and scheduling
• Expertise in Heuristics/Metaheuristics methods
• Highly proficient in Python, Scala and/or Kotlin
• Deep understanding of data structures and algorithms
• Ability to test and measure algorithms/models
• Excellent written and verbal communication skills

Bonus Points:

• Experience in statistical inference, Machine Learning and AI
• Experience deploying models with large-scale business impact in commercial setting
• Experience working with large event-driven distributed systems and multi-threaded applications

Why work with us at Target?

• You will work directly on the problems that have the impact on Target's supply chain. 
• We love open source! Many of our team members contribute to open source communities and get to do it during work time. We try to contribute back to our communities where we can and are grateful to be able to open source some of our own projects!
• We value diversity. We believe that diversity and inclusion is of core importance when try to create positive in-store experiences for our guests, and we think it is also critically important when building our teams.?Read more about our commitment to diversity and inclusion
• We value our team members for who they are, not just what they can get done. We treat out team members like people, not like cogs in a machine. We are parents, hobbyists, enthusiasts, family members, and community members, and can offer flexibility to our team members' schedules and work arrangements so that they can flourish both inside and outside of work.