Sr. Data Scientist, MarTech job opportunity at Gap Inc..

Home Jobs In United States Of America Tag: MarTech Sr. Data Scientist, MarTech position at Gap Inc.


DateMore Than 30 Days Ago bot
Gap Inc. Sr. Data Scientist, MarTech
Experience: General
Pattern: full-time
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MarTech

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degreeGeneral
loacation SF - 2 Folsom, United States Of America
loacation SF - 2 Folsom....United States Of America

About the Role The Martech Data Science Team at Gap Inc. applies AI and machine learning techniques to drive business benefits for Gap Inc. and its brands. The team’s focus is on building capabilities that power personalized customer experiences and optimize marketing to grow customer lifetime value and advance Gap Inc.’s AI transformation. Areas of expertise include advanced predictive modeling, marketing effectiveness, and business growth, as well as developing and scaling models on modern platforms and embedding them into marketing workflows. You will help build and deploy production capabilities, in partnership with Engineering, Product Management, Central Marketing, and business partners across our brands. What You'll Do 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 reliable data pipelines and shared feature sets for various modeling protocols Build, validate, and maintain AI (Machine Learning / Deep Learning) models, diagnose and optimize performance, and develop statistical models and analysis for ad hoc business-focused questions Design and execute structured experimentation and testing (e.g., A/B tests, multivariate tests, RL/bandit experiments) to measure incremental impact, refine models, and continuously improve marketing strategies across brands and channels Communicate meaningful, actionable insights and model outputs to stakeholders, translating them into clear go-to-market recommendations that influence campaign design, offer strategy, contact strategy, and customer experience Build cross-functional partnerships and consensus with engineering, product management, central marketing, and brand strategy teams to integrate models into marketing campaigns, journeys, and agentic AI workflows at scale Influence strategy for the Martech Data Science team, including migration and modernization of models on GCP, accelerating agentic AI–driven decisioning, and strengthening a rigorous, test-and-learn culture across campaigns and customer experiences Who You Are Advanced proficiency in R, Python, Spark, Hive (or other MR), and common scripting languages for E2E pipeline development. Advanced proficiency using SQL for efficient manipulation of large datasets in on-prem and cloud distributed computing environments, such as Azure and GCP-based environments Experience with ML and classical predictive techniques such as logistic regression, decision trees, non-linear regressions, ANN/CNN, boosted trees, SVM, TensorFlow, and visualization packages; familiarity with reinforcement learning / bandit methods and uplift / causal modeling is a strong plus, along with a track record of creating business impact with these methods in marketing or personalization contexts Ability to work both at a detailed level (data wrangling, feature engineering, model diagnostics) as well as summarize findings and extrapolate knowledge to make strong recommendations for change, explicitly linking models and tests to incremental revenue, margin, customer LTV, and contact efficiency Ability to collaborate with cross-functional teams and influence product and analytics roadmaps, with a demonstrated proficiency in relationship building and in operationalizing models into campaigns, journeys, and orchestration platforms Ability to assess relatively complex situations and analyze data to make judgments and recommend solutions, balancing short-term performance vs. long-term customer value, and navigating tradeoffs across revenue, profitability, customer experience, and contact pressure

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