Head of Global Analytics & Insights

Levi Strauss & Co.•San Francisco, CA

2d•$251,600 - $369,100•Hybrid

About The Position

The Head of Global Analytics & Insights will lead Levi Strauss & Co.’s Global Analytics & Insights organization and turn enterprise data into faster, better commercial decisions. The role will build a modern, business-facing capability across performance reporting, decision science, experimentation, consumer insight, and AI-enabled analytics. The position serves as a strategic thought partner to leaders across DTC, wholesale, merchandising, marketing, loyalty, and other functions. The team will frame important business questions, translate evidence into clear choices, and support decisions involving assortment, pricing and markdowns, channel mix, consumer lifetime value, and online and in-store experiences. The role is part of an integrated Data, Analytics & AI leadership team, working alongside Analytics Engineering, Enterprise Data Science & AI, Platform Engineering, and Data Governance. It reports to the Head of Data, Analytics & AI. The position follows a hybrid schedule based at the San Francisco headquarters, typically requiring three days in the office per week, usually Tuesday through Thursday; office attendance may vary with business needs.

Requirements

12+ years of progressive experience in analytics, insights, or decision science.
At least 5 years leading an analytics organization.
At least 3 years leading managers across multiple business domains.
Leadership experience in multi-channel retail, consumer goods, apparel, or another complex consumer business spanning both direct-to-consumer and wholesale or partner channels.
A record of influencing commercial and P&L outcomes through analytics, including areas such as pricing, assortment, promotion, merchandising, channel, loyalty, or consumer growth.
Experience building or evolving an analytics organization, operating model, and capability portfolio, beyond managing steady-state reporting delivery.
Demonstrated adoption of self-service, AI-assisted analytics, or decision products, with evidence of improved usage, trust, time-to-insight, or business impact.
Fluency in experimentation, measurement, and advanced analytics, including sound judgment in selecting appropriate methods and clearly communicating limitations.
Working knowledge of large language models, AI agents, and evaluation approaches sufficient to establish controls and determine when human review is required.
Executive presence and exceptional communication skills, including the ability to turn ambiguity and complex evidence into clear choices, recommendations, and action.

Nice To Haves

Experience in strategy consulting, advanced analytics consulting, or a high-growth consumer or technology company, particularly shaping executive decisions and transformation agendas.
Proficiency with SQL and familiarity with modern BI, semantic-layer, and analytics engineering platforms such as Looker, Tableau, Power BI, and dbt; hands-on coding is not central to the role.
Experience establishing analytics product management, model or AI evaluation, responsible AI, or value-realization practices in a business setting.
Knowledge of apparel or lifestyle retail, including seasonal cadence, fit/size/color analytics, consumer cohorts, and global operations across multiple regions.

Responsibilities

Define a multi-year analytics strategy and portfolio, prioritizing high-value decision opportunities across global commercial domains while balancing enterprise consistency with regional and functional needs.
Partner with executives and domain leaders to frame ambiguous problems, challenge assumptions, identify choices, and establish shared accountability for measurable outcomes.
Translate sell-through, assortment, pricing, promotion, consumer, loyalty, and channel data into decision-ready recommendations, scenarios, and actions.
Modernize insight delivery by moving from static reporting and ad hoc requests toward governed self-service, reusable decision products, proactive signals, and conversational analytics.
Embed AI and agentic workflows into analysis and insight delivery, with evaluation criteria, human oversight, and escalation paths appropriate to decision risk and materiality.
Expand experimentation, causal measurement, and scenario planning so teams can learn faster and allocate resources with greater confidence.
Establish intake, prioritization, service levels, decision rights, and value-tracking practices to focus capacity on important business questions and reduce low-value work.
Lead and develop managers and analytics talent across domains, strengthening consultative skills, commercial acumen, technical fluency, and succession pipelines.
Partner with related teams on semantic standards, reusable data products, model deployment, platform capabilities, data quality, and governance so insights are trusted and scalable.