Principal Data Scientist - Recommender Systems

About the Role

You will lead the development and optimization of AI Systems that drive personalization for Gap’s digital business, working closely with cross-functional teams to deliver personalized and engaging experiences for our customers. At the principle data scientist level, this individual will have significant technical expertise and vision to drive the end-to-end development of complicated real time personalization AI Systems.
This role is located onsite in San Francisco, Pleasanton or Dallas.

What You'll Do

• Build, validate, and maintain AI (Machine Learning (ML) /Deep learning) models focused on customer personalization; diagnose and optimize performance
• Develop software programs, algorithms and automated processes that cleanse, integrate and evaluate large data sets from multiple disparate sources
• Manipulate large amounts of data across a diverse set of subject areas, collaborating with other data scientists and data engineers to prepare data pipelines for various modeling protocols
• Communicate meaningful, actionable insights from large data and metadata sources to stakeholders
• Collaborate with others in key initiatives and their implementation
• Responsible for planning, budget and end results; set policies and strategic direction for area/team

Who You Are

• Direct experience with building real time Recommender systems for digital commerce businesses
• Advanced proficiency in R, Python, Spark, Hive (or other MR), and common scripting languages for E2E pipeline Advanced proficiency using SQL for efficient manipulation of large datasets in on prem and cloud distributed computing environments, such as Azure environments
• Experience with ML and classical predictive techniques such as logistic regression, decision trees, non linear regressions, ANN/CNN, boosted trees, Content/Collaborative filtering, SVM, Tensorflow, visualization packages, and a track record for creating business impact with these methods
• Ability to work both at a detailed level as well as to summarize findings and extrapolate knowledge to make strong recommendations for change
• Ability to collaborate with cross functional teams and influence product and analytics roadmap, with a demonstrated proficiency in relationship building
• Evaluate sometimes complex situations using multiple sources of information (internal and external sources)
• Able to filter, prioritize, analyze, and validate potentially complex In-depth understanding of concepts and procedures within own subject area and understanding of procedures and concepts in other areas