- Developed and enhanced CDI/CDU files to perform cashflows on NPL RMBS deals using Intex.- Performed underwriter tie-outs on 2 RMBS deals.(Yield/WAL tie outs, Cashflow tie outs based on deal structure in indentures using IntexCalc)

- Developed and upgraded the CLO Assets Model used for stress-testing and predictive analysis of CLOs. It was to be used to evaluate/re-evaluate all deals part of the Structured Credit Portfolio. (Concepts & tools: Monte Carlo simulation, Numpy, Pandas, xlwings)- Mapped internal risk grade to DBRS rating for CLOs (worth $400 million) to be used as a standardization for DBRS methodology while evaluating the deals, won the Spot award for Oct. 2021, for this effort (Concepts & tools:… Show more - Developed and upgraded the CLO Assets Model used for stress-testing and predictive analysis of CLOs. It was to be used to evaluate/re-evaluate all deals part of the Structured Credit Portfolio. (Concepts & tools: Monte Carlo simulation, Numpy, Pandas, xlwings)- Mapped internal risk grade to DBRS rating for CLOs (worth $400 million) to be used as a standardization for DBRS methodology while evaluating the deals, won the Spot award for Oct. 2021, for this effort (Concepts & tools: Markov chains, Numpy, Pandas).- Performed Cash Flow and stress-testing analysis of 14 transactions, worth $763 million.- Developed a portfolio mapping dashboard in Python for RMBS using historical data, with options to select Slice and dice through the data and to display key metrics; was awarded Star of the Quarter Award 2021 Q3 for this effort. (Concepts & tools: Dash, Plotly, Pandas)- Computed correlations on Annual Default rates by broad industry groups and Annual Issuer-Weighted Corporate default rates on data (Concepts & tools: Normal distribution, multivariate analysis, Numpy)- Shared codebase ownership in generating the Figure Automotive Securitization tapes report each month along with the ABS team and assisted in updates and bug fixes. (Concepts & tools: Numpy, Pandas) - Developed PowerBI dashboards for management to identify improvement areas for all teams segregated by asset classes. Made group lead of the Operational Efficiency Management focus group in recognition.- Led weekly syncs with on-shore teams and mentored a team of 4 analysts to optimize day-to-day task allocation and train new team members.- Created training videos and process manuals for knowledge retention and smooth onboarding in the team. Show less

• Developed dashboards and management systems to generate business insights, maintain a pulse onbusiness and identify opportunity areas for clients ranging across pharma, education, healthcare andhospitality industries• Role involved interacting with clients to understand business KPIs and objectives; developingdashboards to generate insights; developing documentation and rolling out these dashboards to clients• Worked in teams ranging 2-7 members across 7… Show more • Developed dashboards and management systems to generate business insights, maintain a pulse onbusiness and identify opportunity areas for clients ranging across pharma, education, healthcare andhospitality industries• Role involved interacting with clients to understand business KPIs and objectives; developingdashboards to generate insights; developing documentation and rolling out these dashboards to clients• Worked in teams ranging 2-7 members across 7 projectsLanguages and technologies: Python, Power BI Show less

• Developed a dynamic mass mailing portal that could send heads-up emails to clients 24 hours before server maintenance in a systematic and standardized manner which helped clients plan around the server maintenance schedules and drive greater client satisfaction• Developed scripts for cloud-based automation for greater efficiency in running time-consuming scriptswhich were being run manually on local systems by team members (Skills Learnt: SQL, Python)

• Developed high accuracy (~75%) models using Machine Learning techniques - SVMs, neural networks, etc. in a team of 5. (Skills learned: Keras, pandas, tensorflow Python Libraries)• Case study application: Applied above models to predict credit card default probability based on Taiwan Bank Data from UCI repository using python, R and weka.

• Developed artificial neural networks models, to optimize inputs to maximize yield and minimize cost• Final Model had an accuracy of 84% , based on historical test data • Languages used: R & MS Azure

• Developed engaging audio content in a team of 4.• Developed publicity content for social media using Inkscape.