Foot Locker’s Pivot to Data Science and AI

When a traditional shoe-retailer like Foot Locker creates data-science teams and completely revamps its corporate culture and process around their (young-ish) client base, with initiatives executed across product, user X, design and engineering by the Chief Information and Customer Connectivity Officer, it becomes clear how much AI is changing even the most “sleepy” industries.

A few years ago Footlocker hired an EVP level position, Global Chief Information and Technology Officer, to help create a culture of large scale data mining and predictive analytics to help Foot Locker connect in new and deeper ways with its customers. Pawan Verma came from Target, where he led digital technology and API Platforms, but really cut his teeth at Verizon for over a decade, as IT Leader and Senior Director for Data Architecture and Product Engineering.

A key element of the cultural shift was to vastly increase the tech staff for Foot Locker. A typical job ad today, taken form a posting for Bradenton, Florida, reads:

Seeking a Data Scientist to lead the team in developing sophisticated predictive models, mining large data sets for insights, building scalable data products, and growing the overall Data Science capability at Foot Locker

The ideal candidate possesses the following qualifications.

  • Advanced SQL skills and is comfortable operating with relational data models and structure.
  • Advanced skills with NoSQL databases and can interact with large amounts of data stored in a Hadoop environment.
  • Is capable of accessing data via a variety of API/RESTful services.
  • Advanced programming skills in either R, Python, or similar analytical language.
  • Intermediate knowledge of Linux and Bash. Can interact with the OS at the command line and create shell scripts to automate workflows.
  • Experience with state of the art techniques such as AI and Deep Learning.
  • Advanced knowledge of Apache Spark.
  • Intermediate knowledge of cloud environments such as AWS and Azure.
  • Intermediate understanding of software development and collaboration, including experience with tools such as Git.
  • Exceptional written and verbal communication skills, comfortable presenting in front of large audiences.
  • Excellent data visualization skills, is able to determine the appropriate visualization for a variety of data types and create compelling stories with data.
  • An advanced understanding of supervised and unsupervised learning techniques including; variable selection, feature engineering, model generation, model diagnostics, and deployment.
  • Excellent statistical skills that are grounded in a thorough understanding of testing and frequentist/Bayesian methodologies.


Bachelors Degree, Masters Degree or PhD. in a Data Science, Applied Mathematics, Computer Science or otherwise research-based field; 3-5 years of related experience and/or training; or equivalent combination of education and experienc

Footlocker used to do 4-5 tech releases per year – now they sometimes do 4-5 per day.

Data science, machine learning and artificial intelligence will change and already are changing every industry worldwide, from the bottom up, with individuals experiencing a personal change in their use of technology, and then expecting this change to extend also to their businesses and purchasing decisions.

Start small or start big, but start now.

To read more

Register below for access to the Warren Enskat client portal

Written by

Daniel Enskat

Daniel has written over a dozen books on the global asset management industry and has lectured at universities around the world alongside speakers such as Secretary of State John Kerry, Dr. Mark Mobius, ex-Fed Chairman Alan Greenspan, Professor KC Chan and former Prime Minister Gerhard Schroeder.

He is widely sought after for presentations, discussions and his perspective on the global asset management industry, and in the last two decades has advised hundreds of investment management CEOs on strategy and global expansion.

Scroll to Top