Data Scientist at Foot Locker

Overview
This is a REMOTE Role based in the US
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!
Our Data Scientist work cross collaboratively with other team members and project managers, 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 road-map for projects.
Our Data Scientists work on a wide range of problems improving Foot Locker’s omni channel business. You will be working with large data sets to find opportunities and using models to test the effectiveness of different courses of action. You will be working with data analysis, visualization, the building and scaling of models. 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
Work with lead scientists and scrum masters 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 Apply strong expertise in data science, 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.  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. 
Qualifications
Bachelor’s degree in Business, Marketing, Statistics, Finance, Computer Science or related field from an accredited university or college required.An advanced degree (MS) in mathematics, statistics, physics, economics, computer science, operations research or related technical discipline ir preffered. Minimum of 2 years professional experience’ working in Data Science & Machine Learning.  Experience in the retail/e-commerce domain is a plus. Deep knowledge of machine learning and statistics. An advanced understanding of supervised and unsupervised learning techniques including variable selection, feature engineering, model selection training/testing/validation, 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. Strong SQL skills; Spark/sql, pyspark, databricks. Solid 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. You are a team player with 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. You demonstrate organizational empathy while delivering results. Ability to build relationships quickly, collaborate and lead with courage will be a must.Experience with time series forecasting, optimization (linear programming and others), causal modeling & neural networks, a plus.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. 
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