Architected a machine learning churn prediction pipeline (Pandas, Python, Scikit-learn and APIs) that identified 8% of the workforce at risk, enabling HR to implement targeted retention strategies and reduce attrition. Launched an automated Lead Scoring system utilizing multi-dimensional KPIs to standardize performance ranking; reduced manual evaluation effort by 32% while optimizing the selection funnel for high-potential talent. Enhanced demand forecasting models to predict inventory bottlenecks and supply chain inefficiencies. Designed and implemented a Retrieval Augmented Generation (RAG) system to query internal documentation, combining vector search with LLM-based answer generation. Saving approx. 2hrs of weekly information search for users. Collaborated with cross-functional teams to define KPIs and ensure measurability of efficiency gains.

Automated document management and contract workflows using Python scripts and PostgreSQL, reducing manual admin work and contract drafting time by several hours monthly while improving traceability and minimizing errors. Consolidated dispersed operational data into SQL databases with ETL pipelines for data cleaning, normalization, and validation to enable reliable processing and tracking of financial orders. Translated business pain points from lawyers and financial advisors into practical technical solutions, designing repeatable workflows that improved operational efficiency and data reliability making use of BI platforms for insight communication.