Jithin Kurian

Results-driven Data Scientist with 9+ years of experience in fraud analytics, transaction risk analysis, machine learning, and data-driven decision-making across e-commerce and payment platforms. Skilled in Python, SQL, Pandas, NumPy, Scikit-learn, XGBoost, Tableau, and Power BI with hands-on experience analyzing high-volume transactional and customer datasets to identify anomalies, behavioral trends, and operational risk patterns. Experienced in exploratory data analysis (EDA), feature engineering, predictive modeling, classification techniques, and fraud detection workflows supporting risk mitigation and operational efficiency. Strong background in transaction monitoring, Account Takeover (ATO) analysis, chargeback investigations, and fraud pattern identification gained through experience at PayPal, Amazon, and Lightspeed Commerce. Adept at collaborating with analytics, product, engineering, and operations teams to deliver actionable insights, improve reporting accuracy, and support business-focused analytical initiatives.

• Data visualization
• Docker
• Microsoft Excel
• Power BI
• Python
• SQL
• Tableau

1. Data Scientist

   Lightspeed Commerce

   Aug 2024 to Present

   ON, Canada

   • Analyzed high-volume transaction, merchant, and customer datasets using Python, SQL, Pandas, and NumPy to identify payment anomalies, behavioral trends, and operational risk patterns across commerce platforms. • Supported the development of machine learning models for anomaly detection and transaction risk classification using Scikit-learn and XGBoost, improving identification of suspicious payment activities and fraud-related behaviors. • Performed exploratory data analysis (EDA), statistical analysis, and feature engineering on structured and semi-structured datasets to support predictive analytics and business decision-making. • Built SQL queries and analytical workflows for data extraction, cleansing, validation, and transformation, improving reporting efficiency and data accessibility for business teams. • Developed Tableau and Power BI dashboards to monitor fraud indicators, transaction trends, merchant performance, and operational KPIs across payment and commerce operations. • Evaluated classification model performance using precision, recall, F1-score, and ROC-AUC metrics to support fraud analytics and transaction monitoring initiatives. • Conducted ad-hoc analysis on payment failures, chargeback activity, and customer transaction patterns to support operational investigations and process improvement efforts. • Collaborated with analytics, product, and engineering teams to deliver data-driven insights, improve reporting accuracy, and support analytical solution development.

2. Risk Operations Agent IV

   PayPal India Pvt Ltd

   Sep 2018 to Aug 2023

   Bengaluru, India

   • Contributed to a high-priority fraud mitigation pilot analyzing 8,000+ unauthorized Venmo account cases using SQL, Tableau, and internal risk investigation platforms (Jupiter/Cassini Mars); identified IP-device clustering behaviors and stolen Amex first-transaction fraud patterns, enabling automated rule deployment that reduced manual reviews by 75% (8K → 2K cases) within 4 days and prevented approximately $550K in chargebacks. • Performed large-scale transaction analysis across 350K+ PayPal payment events using SQL to detect Account Takeover (ATO) activities, identifying fraud indicators including geo-IP mismatches, abnormal purchase spikes, device anomalies, and newly linked card behaviors; contributed to rule optimization initiatives that reduced false positives by 11% and saved 150+ manual review hours per month. • Conducted behavioral and historical profile analysis to identify suspicious account activity, derive actionable fraud insights, and support risk mitigation strategies while balancing customer experience and operational efficiency. • Identified emerging fraud, abuse, and financial risk patterns across consumer and seller ecosystems; collaborated on enhancements to fraud prevention rules, operational workflows, and risk decisioning processes. • Partnered with product, operations, and internal risk teams to communicate fraud trends, investigation findings, and process improvement opportunities supporting platform security initiatives. • Led biweekly knowledge-sharing and fraud education sessions focused on investigation quality standards, fraud pattern recognition, and operational review consistency across teams. • Investigated real-time payment transactions and account behaviors associated with Account Takeover (ATO), identity theft, cash advancing, spoof malware, buyer-seller collusion, and money laundering activities. • Performed detailed account investigations involving suspicious transaction activity, policy violations, and abnormal behavioral signals; escalated high-risk fraud cases for advanced review and remediation. • Monitored evolving fraud methodologies (MOs), documented emerging threat trends, and supported internal fraud intelligence reporting initiatives. • Conducted account-level risk assessments to minimize financial exposure while maintaining compliance with operational quality standards and customer service expectations. • Collaborated cross-functionally with fraud operations, compliance, audit, and product support teams to ensure adherence to fraud prevention policies, investigation procedures, and operational controls.

3. Transaction Risk Investigator

   Amazon India Pvt Ltd

   Feb 2016 to Aug 2018

   Bengaluru, India

   • Supported Buyer Risk Investigation operations focused on detecting fraudulent activities, minimizing transactional risk exposure, and improving platform trust and safety for customers and stakeholders. • Conducted financial and behavioral investigations by reviewing customer profiles, transaction histories, payment activities, and fund movement patterns across multiple products and services. • Managed account-level risk reviews and portfolio monitoring activities using historical transaction analysis to identify abnormal behaviors, fraud indicators, and operational risk trends. • Derived actionable insights from transactional and account-level data to support fraud prevention efforts, operational decision-making, and investigation quality improvements. • Documented investigation findings, operational defects, tool limitations, and workflow enhancement recommendations; collaborated with IT development, audit, and quality assurance teams to improve investigation processes and internal tools. • Communicated potential fraud, abuse, and security risk patterns to customers and internal stakeholders while promoting awareness regarding policy violations, unethical conduct, and account security risks. • Assisted in identifying suspicious account behaviors, transaction anomalies, and emerging fraud patterns through manual investigations and policy-based risk assessments. • Maintained compliance with operational procedures, risk management guidelines, and investigation quality standards within a high-volume transaction review environment.

1. Seneca College

   Graduate Certificate, Artificial Intelligence

   Newnham

   2025

2. Seneca College

   Graduate Certificate, Business Analytics

   Newnham

   2024

3. Visvesvaraya Technological University

   Bachelor of Engineering, Electronics and Communication Engineering

   2014

• Google Data Analytics Certification