Back to jobsEngage with internal stakeholders to understand business objectives, translate requirements into analytical projects, and drive end-to-end execution with structured problem-solving.
Gather, clean and validate structured and unstructured datasets from multiple sources, ensuring accuracy, completeness and consistency in data processing.
Perform deep-dive analysis and statistical modeling to identify trends, correlations and business drivers, enabling data-backed decision-making.
Design and develop visually compelling dashboards and reports using Power BI, Tableau, or similar tools to simplify complex data for business users.
Automate recurring reporting and analytics workflows using SQL and Python, reducing manual efforts and improving data accessibility.
Implement predictive models, clustering techniques and forecasting algorithms to support business strategy, risk assessment and operational planning.
Work closely with data engineers, IT teams and business stakeholders to enhance data architecture, ensuring seamless data integration and availability.
Validate, optimize and standardize analytical methodologies, ensuring accuracy, efficiency and best practices in data analysis.
Mentor, train and provide guidance to junior analysts, fostering a culture of knowledge sharing and continuous learning within the analytics team.
Develop and manage AWS data pipelines (Lambda, S3, Glue, Step Functions, EventBridge) that process fraudrelated datasets at scale.
Write production-quality code following best practices for testing, documentation, version control, CI/CD, and secure handling of fraud and transaction data.
Create Python-based visualizations (Plotly, Matplotlib, Seaborn) to support deep fraud analytics, pattern detection, and automated insights delivery.
Translate complex fraud analytics and model outputs into clear, compelling narratives for senior and executive stakeholders.
Own key analytical and data engineering processes that support fraud detection, rule performance monitoring, and fraud operations reporting.
Develop offline fraud detection logic, heuristic approaches, and analytical frameworks to identify suspicious activity, fraud patterns, and anomalies.
Evaluate rule performance, precision/recall, false positives, and emerging patterns to recommend optimizations and mitigate fraud losses.
Partner with Fraud Operations, Fraud Strategy, and Risk teams to translate fraud trends into scalable rule concepts or model features.
Support earlylife monitoring of new strategies, rules, models, and controls to validate effectiveness and identify unintended impacts.
Own and maintain critical fraud reporting, detection workflows, and analytical infrastructure.
