About the Role

What You'll Do

• Build, validate, and maintain AI (Machine Learning (ML) /Deep learning) models, diagnose, and optimize performance and develop statistical models and analysis for ad hoc business focused analysis.  

• 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.  

• Deliver sound, data-backed recommendations tied to business results, industry insights, and overall Gap Inc. ecosystem of technology, platform, and resources.  

• Communicate compelling, data-driven recommendations as well as potential trade-offs, backed by data analysis and/or model outputs to influence leaders’ and stakeholders’ decisions.  

• Build networks across the organization and partners to anticipate leader requests and influence data-driven decision making.  

• Guide discussion and empower more junior team members to identify best solutions.  

Who You Are

• Experience in developing advanced algorithms using machine leaning (ML), statistical, and optimization methods to enhance various business components in the retail sector.  

• Hands-on experience with forecasting models, running simulations of what-if analysis, and prescriptive analytics.   

• Experience with time series analysis, predictive modeling, hierarchical Bayesian, causal ML, and transformer-based algorithms.   

• Experience with creating business impact in supply chain, merchandise, inventory planning, or vendor management using advanced forecasting techniques.  

• Advanced proficiency in modern analytics tools and languages such as Python, R, Spark, SQL.   

• Advanced proficiency using SQL for efficient manipulation of large datasets in on prem and cloud distributed computing environments, such as Azure environments.  

• 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 (Product, Engineering, etc.) and influence product and analytics roadmap, with a demonstrated proficiency in relationship building.  

• Ability to assess relatively complex situations and analyze data to make judgments and recommend solutions.  

• Mid-level career experience in Data Science, Computer Science, Machine Learning, Applied Mathematics, or equivalent quantitative field.   

• People mentoring experience, ability to work independently on large scale projects.  

• Proven ability to lead teams in solving unstructured technical problems to achieve business impact.  

• Full stack experience across analytics, data science, machine learning, and data engineering