Mar 2025 – May 2025 · 2 mos · Dubai, United Arab Emirates · Remote

• Crypto Spot Market Making

Jun 2024 – Mar 2025 · 9 mos · Bengaluru, Karnataka, India · Hybrid

• Crypto Market Making/Trading

Jun 2022 – Jun 2024 · 2 yrs · Bengaluru, Karnataka, India · Hybrid

• 1) Designed and Implemented a framework to identify best performing mutual fund categories and funds, providing 50-150 bps higher
• returns than the respective index and limiting the downside upto 50%
• 2) Optimised multi-asset portfolios based on certain risk metrics (from asset selection to allocation to instrument selection) that beats the benchmark & having 75% to 50% lesser volatility. Created a systematic rebalancing strategy to improve portfolio return by at least 50 bps.
• 3) Implemented a solution to calculate long/short term capital gains/losses using users transaction data and backtest an users portfolio performance if he had invested with us vs his external portfolio by cloning all of his transactions.
• 4) Implemented a momentum based asset rotation framework that beats the major indices by 1.3%-2.8% having 50% less volatility.
• 5) Automation of all important repetitive tasks using modularised Flask api, MySQL database and Docker that makes manual intervention minimal. Including but not limited to, creation of interactive dashboard to review fund/portfolio performance, overlap & asset allocation. Generation of a detailed pdf of customised investment recommendation in an automated fashion. Automated portfolio construction, rebalancing, data preprocessing and dataset generation for the use of tech team and advisors.
• ▪Technologies utilised: Python, Pandas, Numpy, Plotly, Streamlit, Flask, PostgresSQL, MySQL, Docker, SqlAlchemy, Excel, VS Code, Git

Jul 2021 – Jun 2023 · 1 yr 11 mos · New York, United States · Remote

• Part of Global Research Network(GRN) Team of WorldQuant Predictive utilising machine learning/Quant research methods.
• Designed new features, performed exploratory data analysis and applied feature engineering techniques on the data in SQL/Python in order to prepare a better interpretable data to feed into Machine Learning models and to improve the model performance.
• Experimented with bagging, boosting, linear models, regularization, model blending, and stacking etc, optimising hyper parameters using GridsearchCv, RadomizedsearchCv, Optuna etc to design models having better predictability than base models designed by senior researchers for regression and
• classification problems, evaluated model performance based on metrics such as Precision, Recall, Auc_roc, NDCG, MSQNE, RMSE, and MAE etc
• Tech Used: Python(OOPs, Pandas, Numpy, Scipy.stats, Matplotlib, Seaborn, Dask, Statsmodels), Machine Learning(Scikit-learn, Lightgbm, Xgboost etc), SQL, Analytics, Tableau

Jun 2021 – Aug 2021 · 2 mos

• Reviewed weak supervision based models applicable to Financial markets to label 10 K"s , 10 Q's, news articles etc
• Programmed ton of labelling functions in python to label Entities, Relationships, Events, Products, Transactions etc considering the impact on trading, Scraped articles from a business news website using python in order to test the labelling functions accuracy
• Tech Used: Python(Spacy, Snorkel, Regex, Flair, NLTK), Git & Github, Web scraping(Beautiful-Soup, Requests)

Jul 2020 – Sep 2020 · 2 mos

• 1)Helped in writing cases by doing Research and synthesizing information using Sheets and docs, Worked on synthesizing strategies which a company used for survival in bad times and for expansion in the global markets by reading related research papers, news articles from web, annual reports to compare financial performance and different activities from it's peers, synthesizing information to formulate the possibile strategies.
• Worked on integration of information that forms the basis for strategic innovation loop.
• Finding out strategic collaborations, alliance and acquisition of a company and how it helped in expansion and survival during technological disruptions
• 2) Worked on research project on entrepreneurial ecosystem in south east asia(Singapore, Thailand, Indonesia, Vietnam, Malaysia) with special focus on Healthtech, agritech, Edtech and Cleantech.

Aug 2019 – Aug 2019 · 0 mo · Hong Kong

• 1)Selected among 300 delegates from a large number of students and corporates.
• 2) Have been a part of sales & trading case study session and our team consisting of 8 members from Australia, Hong Kong, Taiwan, London, India, won the competition after the session.
• 3) Selected among 80/300 delegates to visit HSBC Hong Kong head office.
• 4) Visited HSBC Hong Kong head office and learnt about the experiences of many investment bankers and managing director of trading division of HSBC in an interactive session.

Jun 2019 – Oct 2019 · 4 mos

• >Developed high-quality financial market signals and predictive models (alphas) using statistical arbitrage that are implemented in real world by employing systematic financial strategies.
• >Incorporated reversion, momentum and volume many more strategies to create high returns with low
• turnover to exploit statistical arbitrage.
• >Developed algorithmic daily re-balancing long-short trading strategies on top 2000 stocks of US market.
• >used scaling to combine two alphas to form a alpha having better returns and Sharpe ratio than both alphas keeping in mind the space used by the model.
• >Rank under 200 in international quant championship.