Lead Data Scientist ICQA

4 days left

Minneapolis, Minnesota, United States
Jan 01, 2017
Jan 25, 2017
Business, Other
Employment Status
Full Time

The 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.


  • 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.


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.


  • 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


  • 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