• Understand detailed usage of products by analyzing user behavior data, translating complex questions into analytical plans, and building robust product metrics. • Run and measure product experiments (A/B tests) from design through interpretation, clearly articulating results and recommendations to stakeholders. • Develop and maintain key performance indicators (KPIs) and operational metrics for product features and overall business health. • Provide actionable insights for Product Managers on user engagement, feature adoption, and growth opportunities to directly inform the product roadmap. • Build deep-dive reports for Executive Leaders on critical business trends and the performance of strategic initiatives, presenting findings in a clear, compelling narrative. • Collaborate with engineering teams on data logging, instrumentation, and ensuring the accuracy of product data pipelines. • Apply statistical modeling and machine learning techniques to solve business problems (e.g., churn prediction, segmentation, propensity modeling). • Design and implement data visualizations and self-service dashboards to democratize data access across the organization. • Partner with cross-functional teams (Product, Engineering, Marketing, Sales) to define data requirements and analytical objectives. • Coach and mentor junior analysts and data scientists on best practices for data visualization, statistical analysis, and clear communication of results.

• Develop and implement advanced analytics on top of noisy, heterogeneous RiskOS data to understand user behavior, product usage, fraud patterns, and workflow effectiveness; translate findings into concrete product and risk strategy improvements. • Architect and build scalable data pipelines and production ML workflows, collaborating with data engineering to ensure robust, reliable, and efficient data processing for both batch and streaming use cases. • Lead the design, execution, and analysis of experimentation frameworks to optimize user journeys, feature adoption, and workflow performance across the RiskOS platform. • Lead the creation and evaluation of Generative AI solutions (LLMs, agents, prompt‑based tools) that automate analytics, power case review and investigation assistants, streamline documentation, and enhance RiskOS workflows and reporting. • Define rigorous evaluation frameworks for GenAI solutions, including offline benchmarks, human‑in‑the‑loop review, safety and hallucination checks, and impact measurement in production. • Partner with platform and engineering teams to define and build core RiskOS data science infrastructure, including feature stores, model‑serving APIs, evaluation services, and monitoring frameworks for both traditional ML and GenAI systems. • Own end‑to‑end deployment of production‑grade solutions: packaging models and GenAI workflows, integrating with RiskOS services, establishing SLAs, and instrumenting telemetry, alerting, and feedback loops. • Develop and automate tools for model evaluation, stress testing, backtesting, and adversarial scenario simulation to ensure robustness and operational resilience—especially in high‑risk fraud and compliance contexts. • Enable product and risk teams through self‑serve analytics and tools: build dashboards, template analyses, and GenAI‑driven assistants that help non‑technical users explore RiskOS data, tune workflows, and debug decisions. • Collaborate cross‑functionally with product, engineering, risk, solution consulting, and customer‑facing teams to translate business requirements into data‑driven solutions and actionable insights, particularly for fraud and risk use cases on RiskOS. • Mentor and provide technical guidance to other data scientists and analysts, modeling best practices in experimentation, software engineering hygiene, GenAI safety, and rigorous model evaluation. • Ensure all solutions adhere to best practices in data privacy, security, and compliance, especially when handling sensitive PII and financial data in regulated fintech and public‑sector environments. • Contribute to company‑wide standards for ML and GenAI explainability, risk evaluation, feature logging, and documentation, helping raise the overall AI bar across Socure. • Communicate complex technical concepts and findings clearly to both technical and non‑technical stakeholders, including executive leadership and external partners.

• Work with analysis of large volumes of data to identify insights and support data-driven decisions; • Develop, implement and optimize machine learning models and algorithms using Python; • Take data and analytics solutions to scale using Data and AI services on GCP; • Write efficient SQL queries and perform optimization for data extraction and manipulation; • Collaborate with cross-functional teams to understand business challenges and turn them into analytical solutions; • Design and implement data pipelines to support machine learning processes; • Create clear and concise visualizations and reports for stakeholders; • Stay up to date on trends and emerging technologies in Data Science and Artificial Intelligence;

• Design, develop and deploy advanced statistical or machine learning models for credit risk, pricing, collections, fraud, and other high-impact business use cases that drive better data-driven decisions • Lead end-to-end delivery of data science initiatives from problem framing and model design through deployment, monitoring and ongoing maintenance • Partner with cross-functional teams including Portfolio Strategy, Engineering, Product, Underwriting, Sales and Collections to integrate models into our applications, and proactively identify and solve problems in critical business areas • Define and set standards for model development, code quality, and documentation; guide technical design decisions across the team • Act as a technical mentor to team members, fostering a culture of continuous learning and rigorous analytical standards • Communicate complex technical concepts and business implications to both technical and non-technical stakeholders • Build and maintain production machine learning pipelines and monitoring systems to ensure models are reliable, scalable and continuously improving