- Master's Degree
- Doctoral Degree
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The Principal Data Scientist leads the team in developing complete analytical solutions, mining extensive data sets for insights, building scalable data products, and enabling the overall Data Science capability at Foot Locker. This role may come from a highly specialized background such as natural language processing, machine learning, deep learning, or operations research. This individual serves as a senior team member, developing bespoke analytical applications, and applying highly specialized techniques. This role is highly versed in a broad range of analytical strategies, and can seamlessly transition between projects, data types, and problems. This role combines the above mentioned analytical skills with a proven track record of leading projects and delivering viable solutions with measurable returns. Lastly, the Principal Data Scientist is comfortable operating in an ambiguous environment where the outcome may be unclear and the future must be created.
Leads initiatives to understand, contextualize, and solve the most challenging problems in Foot Locker’s analytical portfolio.
As the subject matter expert in Data Science, represents the Data team in strategic projects and executive decisions of high significance.
Derives new data sets and values from existing and/or potential data sources.
Accesses data from a variety of sources, including RDMS, NoSQL, or API.
Performs data aggregations, manipulations, and cleaning to ensure data integrity throughout the entire analytical process.
Applies supervised and unsupervised modeling techniques to data to generate predictions and uncover patterns.
Researches, designs, and implements Deep Learning algorithms for complex business problems.
Creates measurement systems to continuously evaluate the performance of data products.
Develops hypothesis statements and applies statistical testing to determine causality and generalize observations.
Research, design, and implement algorithms for complex business problems.
Works closely with the Data Engineering teams to align core capabilities around outcomes.
Decomposes complex problem statements into specific deliverables and requirements.
Builds advanced prototypes and proof-of-concepts to test emergent tools against our current processes and prospective business scenarios.
Supports the leadership team in developing long-term goals and roadmaps.
Continuously scans the Data Science landscape for recent developments and opportunities to ingrate new methodologies into the existing project portfolio.
Creates presentations and delivers results to colleagues, stakeholders, external organizations, and executive leadership.
Leads program review sessions and collaborative exercises to build the team's overall analytical capability.
Thoroughly understands Foot Locker’s retail business model and can seamlessly connect data, context, and analytical solutions.
Mentors junior team members and provides constructive critique on specific projects.
Advanced SQL skills and is comfortable operating with relational data models and structure.
Advanced/expert level skills with NoSQL databases and can interact with large amounts of data stored in a Hadoop environment.
Advance skills with accessing data via a variety of API/RESTful services.
Advanced/expert level programming skills in either R, Python, or similar analytical language.
Strong knowledge of Linux and Bash. Can interact with the OS at the command line and create shell scripts to automate workflows.
Advanced knowledge of Apache Spark.
Strong knowledge of cloud environments such as AWS and Azure.
Experience with popular Deep Learning frameworks such as Tensorflow, Keras, or Caffe.
Advanced understanding of software development and collaboration, including experience with tools such as Git.
Excellent written and verbal communication skills, comfortable presenting in front of large audiences.
Can effectively and succulently decompose highly complex findings to non-technical 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.
A proven track record of leading successful projects and driving measurable, viable results using Data Science techniques.
EDUCATION and/or EXPERIENCE
Masters Degree or PhD. in a Data Science, Applied Mathematics, Computer Science or otherwise research-based field; 5-7 years of related experience and/or training; or equivalent combination of education and experience.