Analyzed and interpreted healthcare operations and financial performance data to deliver actionable insights supporting cost optimization, utilization management, and inventory control. Partnered with finance, procurement, and operations teams to gather, document, and refine business requirements for reporting and performance tracking. Created and maintained daily, weekly, and monthly Power BI and Excel reports covering supply chain costs, inventory aging, and order cycle performance across 150+ hospital networks. Developed SQL-based ETL workflows and automated Python scripts to streamline reporting processes, ensuring timely and accurate data refreshes. Designed interim dashboards and reconciliation reports during platform migration, maintaining visibility into critical operational KPIs. Delivered multi-dimensional dashboards integrating operational, clinical, and financial data, empowering leadership to make data-driven decisions.

Data Analyst at Kpit Technologies, India

June 1, 2020 - November 1, 2022

Collaborated with healthcare clients to analyze service operations and billing performance data, supporting improvements in revenue cycle and SLA adherence. Partnered with business analysts and finance teams to define KPI metrics, reporting requirements, and variance analysis frameworks for healthcare support systems. Built and maintained Power BI and Tableau dashboards highlighting performance, ticket resolution costs, and turnaround efficiency for internal and client reporting. Developed and optimized SQL queries and Python automation scripts for recurring operational and financial reporting tasks, reducing manual effort by 30%. Conducted data validation and reconciliation across multiple systems (Epic, ServiceNow, Oracle DB) to ensure accuracy in clinical and billing datasets. Supported governance and audit initiatives through meticulous documentation of reporting rules, KPIs, and data lineage.

Ai/ML Engineer at Liberty Mutual Insurance

February 1, 2025 - Present

Designed and deployed machine learning models for risk prediction, claims classification, and anomaly detection, improving model accuracy by 25–32% through advanced feature engineering and hyperparameter optimization. Built scalable NLP pipelines using Transformers, BERT, tokenization, and semantic similarity models to automate policy and claims document processing, reducing manual review workloads by 60%. Developed end-to-end ML pipelines in AWS SageMaker covering data prep, training, validation, model registry, and deployment, reducing deployment timelines by 40% and meeting insurance governance standards. Implemented CI/CD workflows for ML services using Docker and GitHub Actions, achieving zero-downtime deployments and improving production stability. Engineered PySpark-based data preprocessing workflows to handle large actuarial, claims, and customer datasets, improving pipeline throughput by 30% and reducing latency. Deployed real-time inference services using FastAPI, enabling

Machine Learning Engineer at Persistent Systems

June 1, 2020 - November 1, 2022

Applied supervised learning methods (Random Forest, XGBoost, SVM) to develop fraud detection and risk-scoring models for BFSI clients, increasing detection precision by 22% and reducing false positives. Designed unsupervised clustering and segmentation models that improved customer targeting efficiency and boosted marketing decision-making for enterprise clients. Built ARIMA and LSTM-based forecasting models to predict demand trends and operational loads, reducing forecasting error by 18% and improving planning accuracy. Automated data ingestion, preprocessing, and feature engineering workflows using Python, Pandas, and NumPy, cutting data preparation time by 40% and improving model turnaround times. Developed interactive dashboards in Power BI and Tableau to visualize KPIs, model performance, and business insights, supporting real-time decision-making for stakeholders. Performed model evaluation, A/B experimentation, and statistical validation to ensure models met production quality,