• Use data to understand our users -- what they need, what they do, how to retain them, and ultimately how to help them find the best dates. • Partner with cross-functional teams to provide long-term strategic insights for our products. • Support business trends/forecasting and model the financial impact of new product ideas. • A strong knowledge querying massive and complex datasets, analyzing, and driving key and actionable insights. • Working with other cross-functional teams like Finance, CRM, Engineering and Business Intelligence; proactively engage other teams to identify and drive better user experience. • Providing internal consulting to product management, marketing, finance, and CRM teams on A/B tests as they move initiatives from concept to market. • Presenting technical content across business stakeholders. • Working as the product owner of revenue and retention strategies for the product. • Serving as the subject matter expert on applying various statistical methodologies towards distilling actionable insights. • Researching issues about customer behavior/changes by analyzing relevant metrics, trends and supporting enterprise level initiatives.

• Define and maintain key product indicators and a structured metrics tree to ensure consistent measurement across exchange and trading operations. • Prepare, visualize, and clearly present data needed for informed and timely decision-making. • Build informative dashboards and reports in Tableau for product, operational, and leadership teams. • Monitor product and trading performance by analyzing core metrics, feature output, execution quality, and market behavior. • Identify inefficiencies, anomalies, or risks — including suboptimal liquidity providers, algorithm behavior issues, or operational bottlenecks. • Evaluate the performance of trading algorithms and extract actionable insights to improve execution quality and profitability. • Conduct statistical and quantitative analyses, including back-tests, regressions, and time-series evaluations to validate hypotheses and support product improvements. • Perform ad-hoc deep-dive analyses based on business and operational requests. • Prepare new analytical reports and implement alerting mechanisms for key metrics and deviations. • Work closely with product and engineering teams to define analytical requirements and support a data-driven approach across the organization.

• Partner closely with two core product squads and their product managers to understand goals, define success metrics, and translate product ideas into concrete analytical plans. • Design and execute experiments on new and existing features, including scoping test populations, determining rollout strategies, and monitoring “do no harm” tests where sample sizes are smaller. • Write high-quality, efficient SQL (primarily in Snowflake via Hex) to investigate product questions, build custom analyses, and create clear, reusable queries that power reporting and decision-making. • Define tracking requirements for new features and collaborate with engineers and designers to ensure the right data is captured from day one, enabling robust analysis over the lifecycle of a feature. • Analyze experiment and feature performance data, synthesize findings into clear narratives, and make concrete, data-backed recommendations to product managers and business stakeholders (including when to roll back changes). • Collaborate closely with the analytics team (including your manager and peer analysts) for code reviews, feedback, and shared best practices, while independently driving your own roadmap and owning outcomes in your domain.

• Structure, analyze, and interpret large volumes of data, turning them into business insights. • Build and maintain interactive dashboards using tools such as Power BI, Amazon QuickSight, Google Data Studio/Analytics, and Kibana. • Support business teams in defining metrics, performance indicators (KPIs), and ad hoc analyses. • Automate reports and analysis processes using Python or R. • Identify opportunities to improve processes, pricing models, and logistics performance. • Collaborate with multidisciplinary teams (Engineering, Product, Logistics, and Commercial) to guide data-driven decisions.