• Job available in 2 locations
  • St. Petersburg, Florida, United States Corporate Office - St. Petersburg 140 Fountain Parkway
  • New York, New York, United States Corporate Office - New York 330 West 34th Street

Overview

Our global house-of-brands inspires and empowers youth culture. Relentlessly committed to fuel a shared passion for self-expression, we create unrivaled experiences at the heart of the sport and sneaker communities through the power of our people. If you want to be a part of something bigger than you can imagine, you’ve come to the right place.To learn more about the incredible impact we’re making on both our local and global communities,Click Here!

The Senior Data Scientist acts as a technical lead in specific domains developing sophisticated predictive models, mining large data sets for insights, building scalable data products, and growing the overall Data Science capability at Foot Locker working with a prioritized roadmap for projects. You act as an expert in a particular domain of science models and works with other data scientists to create solutions to complex business problems. You must be adept at using large data sets to find opportunities and using models to test the effectiveness of different courses of action. Experience in data analysis, visualization, building and scaling models is imperative. You will use your proven ability to drive business results with data-based insights and be comfortable working with a wide range of stakeholders and functional teams. You will leverage your passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Responsibilities

• Lead initiatives to understand, contextualize, and solve the most challenging problems in Foot Locker’s analytical portfolio
• Understand business problems and design end to end analytics use cases. Partner with business tech leads to implement reusable and robust solutions
• strong expertise in machine-learning, data mining, and information retrieval to design, prototype and build the next generation analytics engine and services
• Partner with data science leadership and engagement teams to present solutions and insights to business
• Help maintain and track project goals and roadmaps. Collaborate with software and data engineers to implement and deploy scalable solutions
• Develop complex models and algorithms that drive innovation throughout the organization. This may include initiatives in understanding our Products, Customers, Operations, Footprint, etc.
• Mentor junior team members and provides constructive critique on specific projects

Qualifications

• Advanced degree (MS/PhDs) in computer science, statistics, economics, mathematics, operations research or related technical discipline highly preferred
• Minimum of 2 years’ experience in Data Science & Machine Learning
• Experience in the retail domain is a plus
• Professional attitude and strong service orientation, superb team player
• Strong business acumen and judgement combined with excellent verbal and written communication that can be used to drive our strategy throughout all levels of the organization
• Demonstrate organizational empathy while delivering results. Ability to build relationships quickly, collaborate and lead with courage will be a must
• Knowledge of cloud environments such as Azure, a plus
• Understanding of SDLC collaboration, including experience with tools such as GIT., a plus
• Understanding of Agile methodologies and continuous delivery, a plus

Technical Must Haves:

• Deep knowledge of machine learning and statistics
• 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
• Able to work with big-data. Familiar with Spark/SQL, PySpark, databricks
• Good programming skills in Python or R
• Excellent data visualization skills: able to determine the appropriate visualization for a variety of data types and create compelling stories with data

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