At Innodata, I worked on AI data annotation and prompt engineering projects for ML and LLM models. I designed and evaluated prompts, annotated and validated 10,000+ data samples, and identified model failures and edge cases.
I built a telecom customer churn analysis system using Python and SQL to process 7,000+ customer records. The project included optimized queries, a modular ETL pipeline, and an interactive Power BI dashboard; it improved churn prediction and segmentation accuracy by 30%.
For an insurance analytics and performance monitoring platform, I developed a system for 50+ concurrent users and implemented star-schema models and 15+ DAX measures. Automated validation supported 95%+ data integrity, while reporting automation reduced reporting time by 40%.
I have a Master of Computer Science from Aurora PG College and a B.Sc. in Data Science from Alphores Women’s Degree College. My work and training include Python, SQL, Power BI, and AI data annotation.
