Cheshta Pathak

Analyst – Trust, Safety & Fraud Operations with 2+ years of experience in fraud analytics and data engineering. Proficient in SQL, Python, Pandas, NumPy, PySpark, Databricks, Delta Lake, and Azure Data Factory. Improved fraud detection accuracy (+15%) and reduced manual backlog (–30%) through scalable data engineering solutions.

Analyst – Trust, Safety & Fraud Operations at Capgemini (2024-06 – Present)

• Query large-scale datasets with SQL to detect anomalies in fraud telemetry, strengthening automated detection workflows and reporting accuracy.
• Build Python scripts (Pandas, NumPy) to automate account validation workflows, reducing manual backlog by 30% and improving data processing reliability.
• Design automated fraud detection rule logic and anomaly-validation checks, improving true-positive alert accuracy by 15%.
• Apply Python + Pandas for data cleaning, validation, and pipeline reliability, ensuring accuracy across fraud reporting datasets.
• Investigate 500+ monthly anomalies across communication traffic, profiling high-risk transactions and isolating 200+ flagged records weekly.
• Coordinate cross-functional escalations with engineering and finance teams, cutting containment response times by 40% through structured data handoffs.

B.Tech in Electronics & Communication Engineering – GLA University (2020 – 2024)