• Guide a small class of up to 6 learners through online coursework • Facilitate synchronous discussion sessions each week during the course • Provide personalized asynchronous written and video feedback on projects assigned during the course • Stay abreast of industry trends and emerging technologies, fostering a culture of continuous learning among learners • Connect curriculum concepts with practical, real-world applications • Provide assessment grading and constructive feedback on learners' projects • Lead a small group of learners, fostering a collaborative environment

• Leading the Europe data team in developing a data strategy, roadmap, and resource allocation that maximizes impact for 3D & Mockups, Photo AI groups and Charts team. • Ensure the Europe data roadmap is well aligned with the Design Experience product strategy and Data Science subspecialty direction • Identify opportunities for collaboration and cross-group and cross-supergroup sharing amongst the Europe data scientists • Facilitate connectivity and accountability amongst the Europe data team, improving Canvanaut engagement and retention. • Shaping the future of data’s role in product lifecycle through experimentation, storytelling, and strategic influence. • Helping us extend data literacy and curiosity throughout the region.

Role Description We're looking for a senior analytics professional who can step in and immediately strengthen how we build, scale, and deliver customer analytics for some of the world's most recognized brands. This is a high-impact, hands-on role — you'll be developing reproducible analytical methodologies, building predictive models, and helping shape the next generation of capabilities on our analytics team. You'll work closely with our internal product and strategy teams to translate business questions into rigorous, scalable analytical solutions. You won't just run analyses — you'll build the frameworks and pipelines that make our insights repeatable, reliable, and ready to operationalize. This role is ideal for someone coming from a CRM, media, or customer analytics background who wants to apply their modeling and data engineering skills in the loyalty and customer experience space. Over time, you'll have the opportunity to help us build entirely new service capabilities — particularly in marketing analytics, lifecycle analytics, and CRM optimization. Qualifications - 5–8+ years of experience in customer analytics, CRM analytics, marketing analytics, or a related quantitative field - Strong proficiency in SQL and Python for data analysis, feature engineering, and model development - Demonstrated experience building segmentation, targeting, and/or predictive models (e.g. propensity, churn, LTV, lookalike) - Track record of developing reproducible, well-documented analytical methodologies and model prototypes that can be handed off to engineering teams for productionization - Experience working with large-scale customer or transactional datasets - Ability to work independently, manage competing priorities, and deliver high-quality work with minimal oversight - Strong communication skills — you can explain complex methodology to non-technical stakeholders clearly and concisely Requirements - Experience with Snowflake or another modern cloud data warehouse (BigQuery, Databricks, Redshift) - Hands-on experience with digital/behavioral analytics platforms (Google Analytics, Adobe Analytics, Amplitude, Mixpanel, or similar) - Background in CRM lifecycle analytics — campaign measurement, journey analytics, contact strategy optimization - Familiarity with the loyalty, retail, or QSR industries - Experience mentoring or developing junior analysts - Comfort working in a consulting or agency environment with multiple concurrent projects

• Recommend underwriting, line/limit, and policy changes to responsibly grow approvals and usage (e.g., cutoffs, segmentation, decision logic), using internal behavior, external risk signals, and reject inference techniques. • Develop cross-product customer credit profiles by connecting external credit attributes to on-us behavior, and identify signals that generalize across products vs. are product-specific. • Drive outcomes from uncovering insights in monitoring, to recommending and executing against strategic insights. • Design, execute, and analyze experiments (A/B tests, holdouts, policy tests), turning results into strategic recommendations and roll-out plans. • Intertwine analytics with business and product strategy to craft business proposals that ultimately impact the bottom-line. • Dig into user behavior and “double-clicks” of the data to identify high-impact opportunities for product improvements or targeted outreach, including second-order impacts. • Leverage AI-powered tools and techniques to accelerate analysis, develop and validate predictive insights, and push the boundaries of what’s possible in product analytics. • Share business insights and product enhancement recommendations to senior leadership and stakeholders effectively and concisely.