Lead the Data and Tech Initiatives within the actuarial practice at Oliver Wyman.

• 200+ devices sold in the first year of sales in USA.• 97% sensitivity has been achieved by current product for detecting referable diabetic retinopathy• 180,000 diabetic retinopathy cases detected so far in rural areas with no internet connectivity

Fabulyst is a SaaS solutions company that delivers cutting-edge analytics for retail fashion industry• Advisory for developing deep learning techniques to extract fashion and textile information from images to create granular parameters used for tagging, search and personalization• Made seed investment to develop POC and demonstrate product/market fit with partner client• Assisted with entry and participation in growth accelerator programs like XRC in NYC

Verisk is a ~30B market cap analytics and risk assessment company including Verisk Financial / Argus as one of its brands serving the financial Institutions.• Head risk and forecasting products function; Define Strategy and road map to develop new products and to shape continuous evolution of current products.• Drive data strategy to use transactional Credit Card data covering 95% of industry and Credit Bureau data to drive benchmark predictive analytic products• Drive Agile software development with cross team of quant modelers, software developers, UX designers and Cloud Engineers.

Individual consultant and entrepreneur building products across diverse industries including health care and math education. Product Development includes:Clinical Research Organization• Led development of Clinical trials intelligence platform to provide insights for site selection and patient recruitments by combining Patients diagnostic data, Labs investigators data and global clinical trials information• Led development of automated data curation from clinical trial publications of current clinical trials across the world using NLP and Machine Learning Math tutoring platform (acquired by K-12)• Designed a platform to develop math content, where solution is broken down into granular concepts defined by common core curriculum standards, allowing for student user data collection at granular concept level• Built ‘Recommender Systems’ using Machine Learning techniques based on the concept level data captured on the platformInvestment Management/Banking• Created predictive models to rank order profitable customers• Created models to predict trade failures based on operational risk data.Retail Banking• Created customized credit risk scorecards for direct and indirect auto lending decisions.• Leveraged technology to automate campaign management for cross sell opportunitiesRetail e-commerce• Led product development team to develop customer engagement suite for web-based retail outlets• Developed product feature set to address targeted messaging and campaign effectiveness• Built Response models to score the customer base on likelihood to ‘respond' and ‘unsubscribe'