o Scaled Bluelearn from 27k to 273k users, 2.4k MAUs to 100k MAUs, and 6% to 18% D30 retention by creating quarterly roadmaps and launching 3 products from scratch – BL Work (hiring), Projects (upskilling), and Bluelearn Pro (upskilling)o Set-up data analytics processes at Bluelearn by integrating Mixpanel, setting up user events, and maintaining data dashboardso Introduced a 3-stage feedback process for cross functional team collaboration and increased product shipping speed by 40%o ⁠Scaled BL Work from 0 to 7,000 recruiters and 15,000 applicants, and helped over 4,000 students find their first internshipo ⁠Built an application tracking feature for job applicants and increased D1 & D30 applicant retentions by 1.6x & 1.8x respectivelyo Introduced two AI features – personalized skill suggestions and automated JD generator – and reduced job posting funnel drop offs from 51% to 24%, thereby increasing the number of jobs posted per day on BL Work by 55% o Planned and executed a GTM strategy that increased job applications by 120%, jobs posted by 38%, and successful hire rate by 125%. The strategy involved product niching, college partnerships, recruiter reminder systems, and social media marketingo Ideated and launched projects, an upskilling product that improved successful hire rate by 2x, and scaled it to 27000 enrollso Built a student dashboard for Bluelearn Pro (first revenue product) for 1-on-1 mentorship scheduling, learning roadmap tracking, and project submissions, enhancing user experience and generating an NPS of 55 and CSAT of 4.8/5

o Created financial adjustments for a $200bn semiconductor company to facilitate the transition in its treasury divisiono Built credit rating action trackers for S&P, Moody’s, and Fitch to track rating changes of North American corporateso Built a rating consideration analysis and a peer study for a $5bn gaming company to help it achieve a strong debut Fitch rating

Performed a thorough financial and qualitative analysis of the Indian IT sector with a focus on Infosys.Performed a peer benchmarking analysis to identify the position of Infosys relative to its peers. In the process, created a data sheet with all the important financials of Infosys and its peers. Documented future threats and opportunities in a strategic recommendation report and presentation for Infosys.

Built a mathematical model using Excel and Python in order to detect outlier trades in the data whose traded interest rate is not aligned with the credit rating of the bond. Used isolation trees, cornish fisher z-score, and inter-quartile range models to identify outliers. Came up with a purchasing power model which added a new dimension to the existing models being used by the company.

Coded and backtested intraday trading strategies in Python which generated over 20% yearly returns with less than 10% maximum drawdown in Indian markets. These strategies were based on technical indicators, chart patterns, and candlestick patterns. Performed in-depth equity research of multiple companies and came up with detailed sectoral and firm-specific analyses in the form of stock reports.

Analyzed the Indian foodservice retail sector and came up with metrics to compare the Indian market to its global counterparts. Identified limitations that are restricting the growth of the Indian foodservice industry.

Built quantitative trading strategies using fundamental, technical, and sentiment data sets in order to generate high PnL with limited risks, limited turnover costs, and high sharpe ratios. These strategies surpassed various quality checks related to the stock universe, turnover costs, and correlations with previously built strategies.