Leading and mentoring a team of ML Engineers with a focus on the following initiatives:Establishing and maintaining scalable real-time model inference services that support both tabular and imaging (vision) use cases. Our team has successfully maintained ML-based services with 100% availability over the past two years.Spearheading multiple proof of concepts (POCs) for an in-house generative AI product, collaborating closely with Leadership and Product Management. These efforts have been tailored to diverse needs, targeted towards 50% improvement in time efficiency.Successfully leading the transition and migration efforts for product acquisition, ensuring a seamless integration and alignment with existing systems and processes.Building, enhancing & optimizing (existing) ML Platform [Reduced IT infra cost by 70% by optimizing current ML Frameworks].

In my role as Machine Learning Manager at Advata Platform, I had the privilege to build & lead a team of eight talented individuals, guiding them in the creation and deployment of innovative machine learning solutions.Through fostering an environment of innovation and efficiency, I've guided my team in generating strategic machine learning solutions that drive data-led decisions on a large scale. Our achievements have empowered the company to introduce new solutions and better serve our existing customer base.Designed and lead the efforts for building Model Serving Component, which was used to deploy 5+ ML services for health-care specific use-cases, which lead to increased revenue for the product unit.Designed and lead the development of surgeon analytics dashboard, which maintained data privacy across geographies and had row level policies, in collaboration with the internal product team, the product was launched across EU, US and APAC region and was used by 100s of surgeons.Lead R&D efforts for adding streaming capabilities to the existing product that only had batch-processing capabilities, which increased the product usage by 90% in the Emergency Department, this was done in collaboration with US MLE team.

As a Sr. Data Scientist, I've been at the forefront of various projects and deployments, driving innovation and efficiency.Developed and tailored AI Recommendation Engine serving over 2M users, enhancing health and wellness through personalized prompts. Key features of the system include an Experiment Framework for A/B hypothesis testing, a Nudge Framework for nudge management, a Grafana-based operational dashboard for monitoring the data and ML pipeline, and a KPI Dashboard for monitoring user behavior improvements towards a healthy lifestyle. This project was in collaboration with the Health Promotion Board, Singapore.Designed and developed a product for Utilization and Cost Management for insurance, focusing on two key areas: a Fraud, Waste & Abuse Framework to analyze and assign risk scores to claims and associated entities, processing over 10 million claims; and a Chronic Disease Management system that leverages periodic claims and clinical data to forecast patient risk, cost, and lifetime value.

As a Data Scientist and ML Engineer, I've successfully contributed to diverse projects and deployments.As a key player in an Emergency Department POC Solution, I utilized clinical and attendance data to predict patient load, improving operational efficiency in emergency departments.I have extensive experience in deciphering complex and large datasets (ranging from 1GB to 100GB) in varied formats such as CSV, HL7, Text, and Image, providing crucial insights for data-driven decision-making.

In my capacity as a Co-Founder & Senior Data Scientist, I've spearheaded various successful projects and deployments.Ideated, designed, developed & delivered an AI based Analytics dashboard for a Pharmaceutical Company. With the usage of this system the company was able to save 75% of manual efforts in developing all the reports, increase frequency of reports by 400%.Lead and developed a proof-of-concept AI-based recommendation engine tailored to real estate properties. By analyzing listings and forecasting future trends like pricing and quality of life indices, the engine provided high-profile investors with insightful property recommendations. This solution equipped the real estate agency with a powerful tool to deliver data-driven advice to their affluent clientele.As a consultant, I've worked with numerous small businesses and startups, leveraging data analytics to enhance their daily operations. From optimizing social media advertisements and email campaigns to forecasting demand, my data-centric approach has assisted these businesses in making informed, strategic decisions.

Designed, developed, implemented, and automated Big Data Pipeline which provided instant insights to the leadership team, helping them make correct and fast decisions. Saving 80% of time spent on manual reporting and provided data to DS team for Data Science experiments. Saving 90% of data acquisition efforts of Data Science team.

I worked as a Lead Data Science Engineer, working independently on solving the following problems:1. Created standardized schema to consume data from cloud providers like AWS, Apache Cloud Stack & Google Compute.2. Designed, developed and deployed the Customer Churn Classification Model, which predicted the risk score (probability) for a cloud user, while they were evaluating the cloud service provider in the trial period. This gave the Cloud Service Provider to reach-out to their risky customers and understand their problems. The aim of this solution was to increase customer acquisition & reduce customer churn.3. Designed, developed and deployed the model to forecast the Revenue from Cloud Resources, which can help the campaign team to allocate budget for trial campaigns.4. Designed & developed the data pipeline, that can extract and transform data from the Cloud Service provider database to the ActonCloud's standardized schema, create required features for models & populate data for various KPI's.Also, I worked intensively in writing REST API's and exploring data that could be meaningful to the customer.

I worked as a Lead Data Science Engineer, working independently on solving the following problems:1. Created standard schema for cloud providers like AWS, Apache Cloud Stack & Google Compute.2. Designed, developed and deployed the Customer Churn Classification Model, which predicted the risk score (probability) for a cloud user, while they were evaluating the cloud service provider in the trial period. This gave the Cloud Service Provider to reach-out to their risky customers and understand their problems. The aim of this solution was to increase customer acquisition & reduce customer churn.3. Designed, developed and deployed the model to forecast the Revenue from Cloud Resources, which can help the campaign team to allocate budget for trial campaigns.4. Designed & developed the data pipeline, that can extract and transform data from the Cloud Service provider database to the ActonCloud's standardized schema, create required features for models & populate data for various KPI's.Also, I worked intensively in writing REST API's and exploring data that could be meaningful to the customer.

I was part of a team that developed a product called "CPBM- Cloud Portal Business Manager," which unifies and streamlines the commerce, user management, service provisioning, and operational aspects of running a cloud in a single platform. This product complements Apache Cloud Stack.I worked on complex billing issues related to virtual machines, storage, network usage, and other factors, which required data exploration and experimentation.• Designed and developed a robust user interface (UI) test automation framework.• Worked on developing and implementing algorithms for billing various cloud services.

Developed a robust automation framework using Selenium and TestNG, implemented the Page Object Design Pattern, and created a wrapper over Selenium to increase its stability. The framework was designed to easily accommodate API tests and made it quick and easy to add new test cases, making it easy to maintain. I also worked with the Citrix Japan team to localize the automation efforts.