- Played a pivotal role in aligning the MVP design and features withclient & investor expectations, contributing to securing $2.5M in seedfunding with a post-seed valuation of $42.5M.- Led MVP transformation and delivered product demos to prospectiveclients, scaling the product from 10 beta testers to 100 users andonboarding 200 health networks for 300 quality projects within a year.- Championed "Smart Solutions Search" using NLP and Generative AI,delivering evidence-based solutions with defined KPIs and successcriteria- Built and managed transitions across multiple development teams,optimizing cost and quality; further collaborated with UX designers toenhance user experience.- Implemented a prioritization matrix to align development efforts withhigh-impact business objectives, drove user-centric development withan in-platform reporting tool linked to JIRA service desk, andstreamlined product iteration through client communications andfeedback collection.- Directed product adaptations for the 2nd largest payer network in theU.S., emphasizing iterative refinements based on active user feedback.

- Led the System design and development of TIMS360 SaaS Data Platform- Implemented best practices for development of front-end, back-end, and database management- Maintaining TIMS360 SaaS through feature updates, data-visualizations, and software testing- Collaborating with marketing teams for delivering TIMS360 Software Demo’s

- Performed image processing and segmentation analysis of cardiac MRIs- Produced live visual 3D models to guide catheter during angioplasty – observed a reduced time under anesthesia for catheterized procedures

- Collected, processed, and cleaned raw, de-identified clinical data- Performed exploratory data analysis to identify key trends and patterns, and enriched data through feature engineering- Developed and tested machine learning models (Artificial Neural Networks) for predicting Hepatocellular Carcinoma in patients with longstanding liver cirrhosis- Publication: Reddy R, Imler T. Artificial neural networks are highly predictive for hepatocellular carcinoma in patients with cirrhosis. Chicago, IL. Gastroenterology, 2017 Volume 152, Issue 5, S1193.