Sep 2021 – Oct 2025 · 4 yrs 1 mo · Remote

• DRG Validation Module: Led the development of the project by utilizing a complex LightGBM model to identify inpatient accounts with potentially incorrect DRG codes. Designed and automated the Model Training pipeline on Azure, ensuring seamless deployment and scalability. This initiative significantly contributed to an annual revenue increase of $80M across clients.
• Charge Capture Module: Directed the Module, employing ML classification models, a rule-based engine, and a feedback model to detect outpatient accounts with potential missing charges. This project resulted in an impressive yearly revenue boost of $140M across clients.
• Denial Prevention Using GenAI: Led the implementation of GenAI-driven denial prevention logic to enhance clinical documentation accuracy and reduce claim denials. Developed and tested end-to-end solutions leveraging LLMs to identify documentation gaps from medical records.
• Stakeholder Collaboration: Partnered closely with Product Management and business stakeholders to enhance, plan and to ensure the successful implementation of the data science initiatives.

Jan 2021 – Jul 2021 · 6 mos · Mumbai, Maharashtra, India

• Solution Design: Designed tailored analytical solutions for diverse business challenges by identifying data requirements, creating strategic plans, and delivering results within tight deadlines.
• Advanced Statistical Analysis: Conducted in-depth statistical analyses to uncover actionable insights and identify trends. Designed experiments, tested hypotheses, and built robust models using advanced analytics tools like R and Python.

Feb 2019 – Dec 2020 · 1 yr 10 mos · Chandigarh

• Revenue Forecasting: Developed and visualized day-wise revenue forecasts, delivering actionable insights to support strategic financial planning and decision-making.
• Customer Churn Prediction: Built predictive models to forecast customer churn and utilized prescriptive analytics to identify underlying reasons for attrition, empowering the organization to implement targeted retention strategies effectively.

Jun 2018 – Jul 2018 · 1 mo

• The project involved leveraging stock price data to extract meaningful insights by performing feature transformations and engineering. These refined features are then used to train and run a regression model, enabling accurate stock price prediction.