Sr Data Scientist - Global Supply Chain and Logistics

Target•Sunnyvale, CA

6d•Hybrid

About The Position

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 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 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.

Requirements

Strong knowledge of supply chain optimization – inventory, transportation, sourcing, distribution, fulfillment, planning, etc.
Strong computer science fundamentals and love rock-solid, well-structured, and efficient code: data structures, algorithms, programming languages, and information retrieval
Strong technical and communication skills to deliver with quality and evangelize technical solutions to business, product and engineering partners
Ability to write understandable, testable code with an eye towards maintainability
You constantly think about the end-user for solutions you develop, and prioritize business impact over technical sophistication
Ability to deal with ambiguity, and prioritize progress over perfection
You have an eye for detail, and relentless curiosity for supply chain problems
You have a knack for answering open questions with data
You understand that simplicity is hard but key to achieving maintainability and to being able to continually deliver value to the business
PhD or MS in Operations Research, Industrial Engineering, Computer Science, Mathematics, Statistics, Physics, or related quantitative fields
3+ years of experience leading large-scale implementations of optimization, simulation, machine learning and deep learning modeling at scale
Demonstrated experience writing highly performant code and deploying algorithms in a production environment.
Proficient in predictive and prescriptive algorithms
Proficient in one or more programming languages: Python, R, Kotlin, or Java
Expert at formulating and implementing mathematical models ranging from mixed-integer programming, stochastic dynamic programming and reinforcement learning
Experience in implementing advanced statistical techniques like regression, clustering, PCA, time series forecasting, etc.
Proficient in application/software architecture (definition, process modeling, etc.)
Experience writing production datasets in SQL/Hive or building internal/production data tools for research or experimentation in a scripting language like Python
Able to produce reasonable documents and narratives suggesting actionable insights
Excellent communication skills with an ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives
Self-driven and results oriented; able to meet tight timelines
Collaborative team player with ability to partner effectively across time zones

Responsibilities

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