• Partner with our Product and Business Teams to understand their needs • Develop and implement advanced data science solutions • Collaborate closely with cross-functional teams • Communicate insights and recommendations to the management and other data community members

• Lead the evaluation and continuous improvement of entity resolution and entity linking pipelines. • Debug new builds, identify anomalies, and recommend modeling or system-level improvements. • Define, implement, and maintain scalable performance and quality metrics, leveraging automation and LLM-based approaches where appropriate. • Partner with Engineering to optimize entity linking and ranking systems using Learning-to-Rank and related techniques. • Design methods to assess and classify entity confidence and quality across the graph. • Design and implement a comprehensive data quality framework for graph-based identity data. • Translate abstract quality concepts (e.g., reliability, stability, consistency) into measurable signals. • Identify and operationalize generalized, high-impact predictive signals derived from graph structure, temporal dynamics, and relational patterns. • Collaborate closely with Engineering, Product Management, Compliance, and downstream product teams. • Act as a technical leader within the Identity organization, influencing modeling standards, experimentation rigor, and best practices.

• Analyze large datasets to identify trends, patterns, and actionable business insights. • Develop predictive and forecasting models for sales, demand, and customer behavior. • Partner with marketing, operations, and finance teams to support data-driven decision-making. • Design and implement A/B tests to evaluate marketing campaigns, product changes, or pricing strategies. • Create and maintain data dashboards and automated reports to track business KPIs. • Build and maintain ETL processes to collect and clean data from multiple sources (e.g., Shopify, Google Analytics, CRM, etc.). • Apply machine learning and statistical techniques to solve complex business problems. • Work with engineers and analysts to ensure data accuracy, consistency, and reliability. • Communicate findings and recommendations to stakeholders in clear, actionable ways. • Stay up to date with emerging data science tools, technologies, and methodologies.

• Own end-to-end modelling of LTV, user segmentation, retention, and marketing efficiency to inform media optimization and value attribution. • Collaborate with Paid Media and RevOps to optimize SEM performance, predict high-value cohorts, and power strategic bidding and targeting. • Work closely with Product Insights and General Managers (GMs) to define core metrics, KPIs, and success frameworks for new launches and features. • Conduct deep-dive analysis of user behaviour, funnel performance, and product engagement to uncover actionable insights. • Monitor and explain changes in key product metrics, identifying root causes and business impact. • Work closely with Data Engineering to design and maintain scalable data pipelines that support machine learning workflows, model retraining, and real-time inference. • Build predictive models for conversion, churn, revenue, and engagement using regression, classification, or time-series approaches. • Identify opportunities for prescriptive analytics and automation in key product and marketing workflows. • Support development of reusable ML pipelines for production-scale use cases in product recommendation, lead scoring, and SEM planning. • Present insights and recommendations to a variety of stakeholders — from ICs to executives — in a clear and compelling manner. • Translate business needs into data problems, and complex findings into strategic action plans. • Work cross-functionally with Engineering, Product, BI, and Marketing to deliver and deploy your work.