• Define and Govern Analytical Strategy: Establish the multi-quarter analytical roadmap for a major business area (e.g., Clinical, Growth, or Operations), translating strategic objectives into measurable, actionable data initiatives. • Architect Data Models: Serve as the principal steward of Synapticure’s data models and semantic layers (dbt/Snowflake), ensuring scalability, performance, and data integrity across the enterprise. • Lead Cross-Functional Initiatives: Manage complex, high-impact projects that require integration of diverse data sources and alignment across Product, Clinical, Finance, and Operations teams. • Strategic Partnership: Act as a trusted advisor to senior and executive leadership—framing key questions, guiding methodology, and delivering data-driven insights that shape company direction. • Mentorship & Team Development: Provide technical mentorship to other analysts, establish standards for analytical rigor and reproducibility, and elevate the analytical maturity of the entire organization. • Define and Govern Key Metrics: Create and maintain consistent definitions for enterprise-level KPIs to ensure alignment and accuracy across teams. • Storytelling with Data: Communicate analytical findings through compelling narratives that connect technical results to business and clinical impact. • Champion Data Integrity: Lead by example in maintaining high standards of quality, governance, and documentation across all analytical outputs and infrastructure.

• Analyze player journeys, game mechanics, and engagement patterns to guide game development and feature design. • Conduct deep-dive investigations and exploratory analyses of KPIs to uncover growth opportunities and friction points. • Communicate findings clearly through impactful visualizations and concise storytelling tailored to stakeholders. • Collaborate with stakeholders to define success metrics, experiment frameworks, and data needs. • Design and execute experiments (e.g., AB tests) based on player insights and team priorities. • Drive improvements to our BI ecosystem and data pipelines to support scalable, product-focused analytics. • Own the full analytics cycle: from identifying questions to delivering insights and driving decisions. • Proactively identify trends, anomalies, and emerging behaviors through ongoing monitoring and analysis. • Contribute to a culture of data curiosity and excellence by supporting data democratization across teams. • Apply a product-centric mindset — focusing on delivering business value, not just tasks or reports.

• Set the vision & roadmap for enterprise analytics across Higher Ed, K12, and Early Career • Raise the bar on integrity & consistency, establishing standards for data quality, reporting, and analytical best practices • Evolve our analytics stack in partnership with Pearson’s data teams • Be the executive thought partner who translates data into strategy for senior leaders • Deliver decision‑ready insight: advanced analytics/models to surface market & customer trends • Build data fluency across the business

• Use SQL and other analytical tools to conduct in-depth analysis of Mercury’s customers, transactions, alerts, TM rules, risk ratings, and more. • Use data-driven methods to improve, design, implement, and maintain Mercury’s FCC models, including transaction monitoring, sanctions screening, customer risk scoring, and relevant alert models. • Develop bespoke transaction monitoring rules designed to address Mercury’s specific AML risk. • Partner with Compliance, Product, and Data leaders to translate regulatory requirements into effective analytical frameworks. • Know how to tell stories with data, enabling people to understand the output and meaning of analytics activities in a clear, compelling manner. • Understand both the what and the why of FCC analytics – what are we looking for and why does it matter. • Develop and maintain detailed documentation on the configuration of FCC models – scenarios, thresholds, segments, tuning, etc. – and any changes made to those configurations over time. • Assist with evaluating and tuning existing detection models and rules to reduce false positives while maintaining regulatory rigor. • Develop data-driven methods to identify new typologies, emerging risks, and evolving financial crime trends. • Partner with Model Risk Management to support validation and performance monitoring of models to ensure compliance with internal and regulatory standards.