Gap Inc.
Senior Data Scientist - Customer Data (San Francisco)
Gap Inc., San Francisco, California, United States, 94199
About the Role
This role sits in our beautiful San Francisco hub office.As a Senior Data Scientist for the Customer Data Science team at Gap Inc, you will be part of a highly visible team bringing direct impact to senior leaders across our four iconic brands: Old Navy, Gap, Banana Republic, and Athleta. You will play a pivotal role in leveraging advanced analytics and machine learning to drive growth, optimize customer experience, and enhance operational excellence across Gap Inc brands. This team is chartered with spearheading data-driven initiatives that address the most important and timely opportunities to the Business, empowering leadership to make informed decisions in areas having the highest impact on financial performance and long-term customer equity.Reporting to the Customer Data Science Director, you will focus on delivering a portfolio of predictive models and inferential decision assets while driving a customer-first mindset across business units. You will leverage your expertise in Causal Inference and Econometrics to socialize robust practices in causal impact assessment, KPI forecasting, and elasticities across a large and like-minded data scientist peer group as well as brand-embedded business analysts. As a senior member of the team, your efforts will further mature internal data products designed to measure value across channels and customer dimensions, optimize promotional cadence, and shape marketing decisions across a full suite of paid, owned, and earned marketing.What You'll Do
Modeled Insights and InferenceCreate a comprehensive Causal ML framework to measure impact.Develop approaches to measure cross-price elasticity, SKU demand transference, cannibalization, and other causal treatment outcomes.Apply modern machine learning algorithms, including deep learning, ensemble methods, and LLMs, to analyze large-scale datasets containing customer interactions with product and platform attributes.Guide audience targeting strategy across CRM, digital, and App through customer lifecycle propensity modeling to acquire and retain customers.Leadership and StrategyInfluence design and execution of multivariate experiments, KPI rationalization, and establish measurement protocols with and without controlled setup, arbitrate over statistical and business significance.Communicate complex analytic findings and insights effectively to stakeholders at all levels.Partner closely with product management, Gap Brand ecommerce, and marketing leads to understand business hypotheses, requirements, and opportunities to influence decisions and roadmaps.Partner with Data Engineering, Data Governance, Platform, and Transformation teams to ensure data consistency, accessibility, scalability, and usability for analytic intent.Team and CultureStay abreast of emerging trends, best practices, and tools for acquiring external intelligence, leading the exploration and implementation of new analytics data sources or techniques.Use audience-appropriate visualizations to drive strategic adoption, tell a story, diagnose performance, and identify opportunities.Who You Are
MS/PhD in mathematics, statistics, data science, econometrics, operations research, or similar.Expert level experience as a data scientist in predictive solution development and revenue impact measurement.Established experience working with customer-level behavioral data, preferably with a multi-channel retailer.Advanced proficiency using SQL for efficient manipulation of large datasets, Azure Databricks.Demonstrated ability to research, evaluate, and apply appropriate methodologies over a broad array of business interests, overseeing causal inference; Causal Impact, synthetic control, diff-of-diff.Python experience directed towards statistical modeling, machine learning algorithms (regression and boosting trees, SVM and similarity methods, time series) and a track record for creating business impact with these methods, preferably in service of acquisition and retention goals.Critical thinking, agile mindset, strong written and verbal communication skills exercised across all levels of business hierarchy, with a demonstrated appetite for relationship building.Passion for retail, fashion, and consumer behavior, with a deep understanding of the digital commerce ecosystem and industry trends.
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This role sits in our beautiful San Francisco hub office.As a Senior Data Scientist for the Customer Data Science team at Gap Inc, you will be part of a highly visible team bringing direct impact to senior leaders across our four iconic brands: Old Navy, Gap, Banana Republic, and Athleta. You will play a pivotal role in leveraging advanced analytics and machine learning to drive growth, optimize customer experience, and enhance operational excellence across Gap Inc brands. This team is chartered with spearheading data-driven initiatives that address the most important and timely opportunities to the Business, empowering leadership to make informed decisions in areas having the highest impact on financial performance and long-term customer equity.Reporting to the Customer Data Science Director, you will focus on delivering a portfolio of predictive models and inferential decision assets while driving a customer-first mindset across business units. You will leverage your expertise in Causal Inference and Econometrics to socialize robust practices in causal impact assessment, KPI forecasting, and elasticities across a large and like-minded data scientist peer group as well as brand-embedded business analysts. As a senior member of the team, your efforts will further mature internal data products designed to measure value across channels and customer dimensions, optimize promotional cadence, and shape marketing decisions across a full suite of paid, owned, and earned marketing.What You'll Do
Modeled Insights and InferenceCreate a comprehensive Causal ML framework to measure impact.Develop approaches to measure cross-price elasticity, SKU demand transference, cannibalization, and other causal treatment outcomes.Apply modern machine learning algorithms, including deep learning, ensemble methods, and LLMs, to analyze large-scale datasets containing customer interactions with product and platform attributes.Guide audience targeting strategy across CRM, digital, and App through customer lifecycle propensity modeling to acquire and retain customers.Leadership and StrategyInfluence design and execution of multivariate experiments, KPI rationalization, and establish measurement protocols with and without controlled setup, arbitrate over statistical and business significance.Communicate complex analytic findings and insights effectively to stakeholders at all levels.Partner closely with product management, Gap Brand ecommerce, and marketing leads to understand business hypotheses, requirements, and opportunities to influence decisions and roadmaps.Partner with Data Engineering, Data Governance, Platform, and Transformation teams to ensure data consistency, accessibility, scalability, and usability for analytic intent.Team and CultureStay abreast of emerging trends, best practices, and tools for acquiring external intelligence, leading the exploration and implementation of new analytics data sources or techniques.Use audience-appropriate visualizations to drive strategic adoption, tell a story, diagnose performance, and identify opportunities.Who You Are
MS/PhD in mathematics, statistics, data science, econometrics, operations research, or similar.Expert level experience as a data scientist in predictive solution development and revenue impact measurement.Established experience working with customer-level behavioral data, preferably with a multi-channel retailer.Advanced proficiency using SQL for efficient manipulation of large datasets, Azure Databricks.Demonstrated ability to research, evaluate, and apply appropriate methodologies over a broad array of business interests, overseeing causal inference; Causal Impact, synthetic control, diff-of-diff.Python experience directed towards statistical modeling, machine learning algorithms (regression and boosting trees, SVM and similarity methods, time series) and a track record for creating business impact with these methods, preferably in service of acquisition and retention goals.Critical thinking, agile mindset, strong written and verbal communication skills exercised across all levels of business hierarchy, with a demonstrated appetite for relationship building.Passion for retail, fashion, and consumer behavior, with a deep understanding of the digital commerce ecosystem and industry trends.
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