Lead Data Scientist - Supply Chain Analysis and Fulfillment

Employer
Location
Minneapolis, Minnesota, United States
Posted
Sep 18, 2016
Closes
Feb 24, 2017
Category
Management
Employment Status
Full Time
PRIMARY FUNCTIONThe Lead Data Scientist will support the development of inventory control and quality assurance visibility/metrics/reporting/analytics programs in service of Target's long term supply chain strategy.  This role will design new statistical capabilities and predictive analytics required to enable world class inventory health, quality, and accuracy across the organization. 

This role will leverage analytical capabilities to build statistical/predictive models to gather, summarize, and analyze data which solve highly complex business problems related to inventory quality and anticipate future defects in process flows. They will construct advanced data models used to predict future business outcomes, support process engineering/development, and drive decision-making on how to error-proof future process from inventory quality issues.  Through their work, they will design, build and deploy new standards and practices in statistical data modeling, big data mining, and machine-learning tools in support of Target's supply chain strategy.

PRINCIPLE DUTIES AND RESPONSIBILITIES

Responsible for designing and deploying data-science and technology-based algorithmic solutions to support inventory accuracy and quality through Target's supply chain.

Implement solutions to current and future business problems using data analysis, data mining, optimization tools, and machine learning techniques and statistics.

Establish statistical foundation for inventory audit programs to provide visibility and reporting on inventory integrity/accuracy across Target's supply chain.

Design predictive programs (utilizing linear regression, logistic regression, probability theory, stochastic modeling, Monte Carlo methods, et al) to address issues proactively and be built into process to correct, reduce, or eliminate potential points of defect.

Utilize data science and machine learning to invent and deploy data and statistical analysis, interpretation, reporting, and utilization across multiple business owners such as HQ, DC field teams, Direct to Guest, and Direct to Store business process teams, etc.  

Build root cause analysis reporting to provide specificity/detail in analytical output(s) to drive visibility and deep understanding of the problems/defects/action across core process teams and partners.

Design user-friendly and drillable reporting for quality check points across each inventory touch through the value chain.

Collaborate with enterprise data experts from business intelligence teams across the organization to seek, understand, validate, interpret, and correctly use new data elements

Define and interpret problems and provides solutions to business problems using data analysis, data mining, optimization tools, and machine learning techniques and statistics.

REPORTING/WORKING RELATIONSHIPS:

Reports to the Director of Operations Research, Inventory Control & Quality Assurance.  Works within the Operations Research team and has direct working relationships with Process Engineering, EDABI, Global Supply Chain, Merchandising, Operational Excellence, Stores, TTS, and third party providers.

MINIMUM REQUIREMENTS:

M.S. in math, advanced statistics, physics, bio statistics, bio science, operations research and/or computer science

3+ years of experience deploying algorithms in a production environment

Experience designing algorithms

Experience creating and deploying solutions based upon big-data technologies and custom-created algorithms

A strong passion for empirical research and for answering hard questions with data

Excellent written and verbal communication skills

DESIRED REQUIREMENTS:

PH.D in math, advanced statistics, physics, bio statistics, bio science, operations research, computer science

Experience writing SQL statements and developing code used to manage and summarize big data

Experience in inventory control/quality assurance

Experience in supply chain

Experience in statistical program development and deployment

Experience with regression methodologies and machine learning techniques