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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
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
Communicate meaningful, actionable insights from large data and metadata sources to stakeholders
Responsible for quality of services and advice in meeting business partner needs
Responsible for end results of team and shares responsibility over resources, budget and adherence to policies
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, 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
Ability to assess relatively complex situations and analyze data to make judgments and recommend solutions
Skills Required
- Advanced proficiency in R, Python, Spark, Hive or other MapReduce technologies, and scripting languages for end-to-end data pipelines
- Advanced proficiency in SQL for manipulating large datasets in on-premises and cloud distributed computing environments, including Azure
- Experience with machine learning and classical predictive techniques, including logistic regression, decision trees, nonlinear regression, ANN/CNN, boosted trees, SVM, and TensorFlow
- Experience with visualization packages and a demonstrated track record of creating business impact with analytical methods
- Ability to summarize findings, extrapolate knowledge, and make strong recommendations for change
- Ability to collaborate with cross-functional teams, influence product and analytics roadmaps, and build relationships
- Ability to assess complex situations, analyze data, make judgments, and recommend solutions
Gap (gapinc.com). Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Gap (gapinc.com). and has not been reviewed or approved by Gap (gapinc.com)..
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Healthcare Strength — Comprehensive medical, dental, and vision coverage is offered, alongside programs that support physical, mental, and financial wellbeing. Feedback suggests eligible employees can also leverage tools like FSAs and additional wellbeing resources.
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Leave & Time Off Breadth — Paid time off, company-paid holidays, and multiple leave options (sick, disability, and family leave) create broad time-away coverage. Some roles start with substantial PTO accrual and can access flexible leave arrangements.
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Wellbeing & Lifestyle Benefits — A generous cross-brand merchandise discount is a standout perk, complemented by commuter benefits, on-the-clock volunteer hours, and matching donations. Feedback suggests these lifestyle benefits add meaningful value beyond base pay.
Gap (gapinc.com). Insights
What We Do
In 1969, Don and Doris Fisher opened the first Gap store on Ocean Avenue in San Francisco. They wanted to make it easier to find a great pair of jeans, and they did. Their denim and records store was a hit, and it grew to become one of the world’s most iconic brands. Today we’re represented in more than 1400 stores in over 40 countries, and online. We have headquarters in New York, London, Shanghai, Tokyo, and, of course, San Francisco. Our unique aesthetic is optimistic cool, elevated American style. Our clothes are crafted with care, with focused attention to thoughtful design. We believe in staying true to our heritage while creating what’s next. Don and Doris Fisher always wanted to “do more than sell clothes.” They wanted to support the people who ran their company, to be active in their communities, and to have a positive impact on the world. Their vision helped transform retail, and we’re still following their lead. We stand for freedom and possibility for all; we champion diverse ideas that transcend generations, geographies and genders.
