• Define and oversee standards for data organization, labelling, and validation across multiple datasets. • Establish practical frameworks to improve consistency, accuracy, and usability of structured and semi-structured data. • Ensure datasets are reviewable, auditable, and suitable for downstream analytical or software development work. • Develop repeatable workflows for data ingestion, cleaning, documentation, and version control. • Identify appropriate tools and practices to support efficient data operations without premature complexity. • Create clear documentation that enables continuity as the team scales. • Manage and mentor a distributed, India-based team performing data preparation and technical support tasks. • Translate high-level requirements into clear, actionable work-streams. • Review outputs and provide feedback to maintain quality and consistency. • Work directly with Vulcury leadership to align data efforts with near- and medium-term development plans. • Support future engineering and analytics hires by delivering well-prepared datasets and clearly defined processes. • Contribute input to planning for subsequent phases of technical development.

• 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.