About
Detail-oriented Data QA & Analytics Associate with 2+ years of experience at Amazon, specializing in high-volume data extraction, validation, anomaly detection, and quality analysis within production-grade ML data pipelines. I work at the intersection of data quality and analytics — surfacing trends, detecting edge-case anomalies, and ensuring integrity of large-scale datasets powering Amazon's AI/ML systems. Toolkit: Python (Pandas, NumPy, Regex), SQL, Advanced Excel, Power BI, and Google Analytics — applied daily in SLA-driven production environments. ↳ Data Pipeline QA: Validated millions of records for anomalies, outliers, and inconsistencies in structured ML datasets ↳ Python & SQL Analytics: Applied Pandas, NumPy, and Regex for data cleaning, pattern matching, and trend analysis ↳ Anomaly Detection: Investigated ML model output anomalies through structured root-cause analysis with documented defect findings ↳ Cross-Functional Collaboration: Partnered with SMEs and international teams across time zones to standardize quality benchmarks ↳ E-commerce Data: Vendor/merchant data management, email categorization, data consistency monitoring Certifications: Google IT Automation with Python (Coursera) | Python Programming — NPTEL (IIT Madras) | Advanced Google Analytics (Google) | Process Mining Fundamentals (Celonis) Open to global Data Analyst, Business Analyst, Data QA Analyst, Analytics Engineer, and BI Analyst roles — remote or hybrid, worldwide. Let's connect to discuss data, analytics, and building quality-driven data systems. abhi86513495@gmail.com
Work experience
ML DATA ASSOCIATE at Amazon, Sep 2024 - Present · 1 yr 10 mos, Bengaluru
Frontend Developer Intern at Kristu Jayanti College Autonomous, Jun 2023 - Jul 2023 · 2 mos, Bengaluru, • Developed an Attendance Management System with responsive UI using HTML, CSS, and JavaScript; translated data requirements into functional, accessible interfaces collaborating with cross-functional teams. • Conducted user research and usability testing; applied data-informed design decisions to optimize system accessibility and improve data input accuracy.
