Vartika Srivastava

About Vartika Srivastava

Business Intelligence Engineer / Business Analyst with experience at Amazon developing LLM-driven workflows, scalable data pipelines, automated analytics solutions, and ML-powered insights that improve decision making and operational efficiency. My work spans end-to-end ownership: translating business requirements into metric definitions, designing ETL workflows using Python, SQL, and PySpark, validating data quality at scale, and delivering dashboards adopted by business and product teams. I enjoy solving ambiguous data problems, delivering insights that impact customers, revenue, and product strategy, and applying statistical and machine learning techniques to real-world business use cases.Core Skills:• Data Engineering and Analytics: Python, SQL, PySpark, Extract Transform Load (ETL), AWS, Data Modelling, Statistical Analysis• BI & Visualization: Amazon QuickSight, SageMaker, Tableau, KPI design, MS Excel, Automations, Scalability, Dashboarding• Applied ML/NLP: Sklearn, Ensemble Models, Prompt Engineering, Transformers (BERT family models), Anomaly Detection, Root Cause Analysis, Explainable AI, Image Processing

Experience

1. Business Analyst

   Amazon

   Nov 2024 — Present · Bengaluru, IN

   Redesigned a legacy metric computation system into a scalable PySpark-based pipeline processing 7Bn products across 20 marketplaces, reducing computation time by 96% and improving memory efficiency by 98%| Deployed an LLM-powered automation delivering E2E defect-detection workflows, cutting manual audits by 85%, improving detection precision by 32%, and accelerating catalog corrections that contributing to $105M iGMS uplift | Built an automated anomaly detection pipeline using IQR + Isolation Forest to flag abnormal return spikes at ASIN, seller, and category level, enabling early defect identification and preventing recurring revenue loss | Developed a Composite Data Quality (CDQ) audit system that auto-routed defective products for correction, driving a 13% improvement in attribute accuracy and reducing customer confusion on size/variation selection | Owned end-to-end analytics delivery, from KPI definition and ETL design to BI dashboard deployment, collaborating with product, retail, and ops stakeholders while maintaining production pipeline reliability

Education

• University of Delhi

  Bachelor of Science (B.Sc) Honours, Mathematics

• SVKM's Narsee Monjee Institute of Management Studies (NMIMS)

  Master of Science (M.Sc), Applied Statistics and Analytics

• Stella Maris Lucknow

  ISC, Science

• Stella Maris Lucknow

  ICSE, Science