• Perform extensive SQL-based data transformations, integrating data from multiple internal and external sources (e.g., claims, media, CRM, EHR-derived datasets). • Define, validate, and govern metrics aligned to pharmaceutical business questions (e.g., brand performance, HCP engagement, patient journeys). • Apply statistical reasoning to analyze trends, identify drivers of performance, and ensure analytical rigor in insights and recommendations. • Maintain and iterate on analytical datasets and workflows as new data is ingested. • Conduct thorough exploratory data analysis (EDA) to uncover patterns, anomalies, and opportunities within complex healthcare datasets. • Interpret and translate analytical findings into clear business insights, tailored for stakeholders across marketing, analytics, strategy, and leadership teams. • Manually scrub, structure, and normalize data as needed to ensure data quality and analytical readiness, particularly for deep-dive analyses. • Prepare datasets to support advanced analytics, including exploratory and explanatory machine learning techniques, ensuring accuracy, consistency, and scalability. • Develop data visualizations and dashboards that clearly communicate insights and support decision-making. • Collaborate cross-functionally with data science, technology, media, strategy, and client-facing teams to align analyses with business needs. • Document methodologies, assumptions, and data limitations to support transparency and reproducibility.

• Design, build, and maintain the data models, dashboards, and reporting frameworks that give the commercial organisation clear, trusted visibility across the full commercial funnel. • Use AI tools as a core part of your analytical workflow — accelerating insight generation, improving output quality, and freeing up capacity for higher-order problem solving. • Run deep-dive analytical projects to surface patterns, anomalies, and opportunities in commercial data, translating findings into clear and actionable recommendations for commercial stakeholders. • Define and track KPIs that measure both business outcomes and the effectiveness of the processes driving them, creating a continuous improvement feedback loop. • Own preparation and follow-through for key internal meetings, strategic reviews, and cross-functional forums — ensuring the right data is in the room and decisions translate into clear next steps. • Build the analytical frameworks that underpin quarterly planning, forecasting, and target-setting, producing outputs commercial leaders can use directly. • Map, challenge, and rebuild core commercial workflows — including pipeline management, forecasting cadences, and sales incentive structures — into cleaner, faster, and more scalable ways of working. • Implement operational improvements end-to-end: not just designing the solution, but driving adoption and measuring impact with rigor. • Identify where AI can fundamentally change how commercial processes work, then build and deploy those solutions directly. • Develop a hands-on understanding of our commercial tech stack, diagnosing where friction, data loss, or missed opportunities exist. • Translate commercial problems into well-defined briefs that Product and Engineering teams can act on, bringing enough technical fluency to be a genuine peer in those conversations.

• Lead analytical efforts to analyze complex data to ensure its integrity and accuracy • Play a key role in driving organizational trust in reporting and analytics • Ensure adherence to regulatory requirements • Collaborate with engineering, data excellence, pricing and analytics, finance, and compliance teams to improve data quality • Proactively query databases, review data and dashboards, understand data flow processes, and support implementation of data initiatives

Role Description Develop and implement a set of techniques and analytics to transform raw data into meaningful information using data-oriented programming languages and visualization software. - Design and implement innovative analytical solutions and work alongside the product and engineering teams. - Apply data mining, advanced feature engineering techniques, and natural language processing to extract and analyze information from large structured and unstructured datasets. - Evaluate new features and architecture that will form the basis for innovative data solutions at Sephora. - Build comprehensive analytics views of predictive drivers and forecast their growth to perform opportunity sizing. - Work from home in the United States permitted. Requirements - Job Site: 350 Mission Street, Floor 7, San Francisco, CA 94105. - 40 hours/week; $133,952–205,196 / year Company Description At Sephora, beauty is about feeling seen, valued, and empowered, individually and collectively. It is connecting deeply with others, celebrating diversity and inclusivity, unlocking your potential, and making a difference every day. Together, we belong to something beautiful.