Responsible for Research and Development, Testing, and Scaling of Recommendation Algorithms for personalised news feed in the Inshorts news app.- Developed, end to end, a novel hyper-personalised recommendation algorithm for user-level personalised news article recommendations. Achieved ≈20% increase in average time spent by users on Inshorts app.- Used LLama2 and ChatGPT for news summarisation and vectorisation.- Used Prompt Engineering for Entity Extraction from news and to divide users into topic cluster buckets.- Mitigation of critical challenges in scaling the algorithms to run realtime for over 1 million daily active users on Inshorts app.- User segment specific fine tuning of algorithm's parameters for further performance optimisation. - Close collaboration with Backend team, App team and Data Engineers.

- Development of models required for assessing the performance of VaR models (in relation to P&L) andquantifying the deficiencies in VaR models.- Automation and Improvement of the efficiency and reliability of the existing processes.- Developed a scalable framework for the attribution of daily portfolio P&L to market risk factors and the generation of automated text commentary. Product being used daily across business units, saving 200+ hours weekly for the team and eliminating operational errors.

Developed Framework to Automate P&L Attribution Commentary for Value-at-Risk P&L Breaches.