Gap is looking to solve the problem of providing personalized and engaging experiences for their customers through the development and optimization of AI Systems that drive personalization for their digital business
Requirements
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
Ability to work with large data sets and metadata sources
Experience with data pipelines and data engineering
Proficiency in relationship building and influencing product and analytics roadmap
Responsibilities
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
Other
Ability to collaborate with cross functional teams
Ability to influence product and analytics roadmap
Able to filter, prioritize, analyze, and validate potentially complex situations
Able to work both at a detailed level as well as to summarize findings and extrapolate knowledge to make strong recommendations for change
Located onsite in San Francisco, Pleasanton or Dallas
