Data Scientist Resume

SUMMARY

The Data Scientist leads 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. This individual serves as the senior member on project teams, often creating solutions from scratch, or, applying known and proven techniques effectively. This role is well versed in multiple analytical techniques, and can seamlessly transition between projects, data types, and problems.
Accesses data from a variety of sources, including RDMS, NoSQL, or API.
Performs data aggregations, joins, 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.
Develops hypothesis statements and applies statistical testing to determine causality and generalize observations.
Solves problems by applying a variety of predictive modeling/machine learning/deep learning techniques.
Maintains a comprehensive understanding of the current analytical landscape, including emergent technologies and methods.
Works closely with the Data Engineering and Product teams to ensure the successful completion of projects.
Decomposes problem statements into specific deliverables and requirements.
Supports the Product Owners in developing sprint 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 and executive leadership.
Leads peer 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 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.
A basic understanding of Agile methodologies and continuous delivery.
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.

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