• Build data products and algorithms that optimize marketing performance—including budget allocation, targeting strategies, and campaign optimization across multiple channels. • Design and implement measurement strategies for marketing initiatives, including attribution modeling, incrementality analysis, and multi-touch attribution. • Drive marketing strategy through data insights, A/B testing, and causal inference methodologies to maximize ROI on marketing spend. • Develop and maintain analytics pipelines and data infrastructure, working collaboratively with data engineers and other data scientists. • Conduct exploratory analysis to identify growth opportunities, understand customer behavior, and optimize marketing tactics. • Provide business-as-usual analytics support including reporting, dashboards, and ad-hoc analysis for marketing stakeholders. • Leverage AI tooling to handle operational tasks efficiently and focus your expertise on high-impact strategic problems. • Partner with ad platforms (Google, Meta, etc.) and internal engineering teams to ensure robust tracking, data integration, and measurement capabilities. • Take ownership and ship it—continuously look for the simplest, most effective way to approach challenges; Occam's razor is your friend.

• Be the dedicated data science partner to the Consolidation product team, owning the analytics and modeling that power product-led growth across onboarding, activation, and full consolidation. • Design and analyze rigorous product experiments across the client journey, from first funding through to full asset consolidation, using A/B testing, non-inferiority tests, and other causal inference methods to measure the impact of product changes on key growth metrics. • Partner with the team's Personalization DRIs to incorporate machine learning models, such as next-best-account recommendations and personalized value propositions, into product flows where they will deliver the highest impact to clients. • Develop deep product analytics to understand how clients move through the consolidation funnel, linking external accounts, reaching their first $5k, adopting multiple products, and identify where the biggest opportunities exist to accelerate that journey. • Create and maintain data models and dashboards that monitor consolidation KPIs (Premium+ conversion, registrant-to-funded rates, linking rates, activation milestones) with a focus on data quality, accessibility, and efficiency. • Collaborate cross-functionally with Product, Engineering, Growth, and Lifecycle Marketing to translate analytical findings into product decisions and shipped features. • Proactively identify and size growth opportunities by being closest to the data, and then partner with stakeholders to get those opportunities prioritized and executed. • Build automation tools and pipelines that increase team efficiency and scalability. • Contribute to the team's culture of learning and knowledge sharing. Teach and learn from your teammates, and help make others successful. • Take ownership and ship iteratively; believe that simple is better and Occam's razor is your friend.

• Own the end-to-end analytical and modeling strategy for Account Transfers, operating with high autonomy in a complex, ambiguous problem space with material client and business impact. • Execute deep investigations across large, complex structured and unstructured datasets to uncover systemic operational failures, root causes, and improvement opportunities within the transfers ecosystem. • Design and build scalable data models and analytical frameworks that enable proactive identification of transfer failures, early risk detection for at-risk client transfers and actionable signals for CXO and Product teams. • Apply advanced modeling techniques (e.g., clustering, segmentation, predictive modeling, causal analysis) to identify distinct transfer behaviour archetypes, surface institutional-level patterns across external counterparties, and inform differentiated operational and product strategies. • Partner closely with Product, CX, Operations, and Engineering leaders to translate analytical insights into product roadmap decisions, operational process design, and client-facing interventions that measurably improve outcomes. • Build and productionize data products (ML models, dashboards, decision tools) that embed analytics directly into workflows and decision-making. • Measure and communicate impact, tying analytical work to improvements in transfer success rates, client experience, operational efficiency, and retention. • Act as a technical and analytical leader by: • Defining best practices for modeling, experimentation, and measurement in the transfers domain. • Mentoring senior data scientists and raising the bar for analytical rigor. • Influencing data strategy beyond immediate team boundaries.

• Own experimentation for the product squad • Size opportunities and guide roadmap decisions • Monitor and investigate squad metrics • Ensure data quality and logging coverage • Grow toward predictive modeling • Work with an incredible team for continuous learning and improvement • Create meaningful social products that connect people