I am a Data Scientist specializing in the application of Machine Learning and Optimization techniques. My expertise lies in translating raw data into actionable insights. Proficient in Python, SQL, Pyspark, R, Hive, Hadoop, and AWS EMR, I also possess skills in data visualization tools such as Tableau.
Currently, I am pursuing an MS in Computational Data Science at Georgia Tech and will be interning as a Quant Analyst in NYC for Summer 23. Prior to my studies at Georgia Tech, I worked as a Data Scientist at American Express, focusing on Credit and Fraud Risk. I hold a Bachelor of Technology degree from IIT Kharagpur, where I majored in Aerospace Engineering with a micro specialization in Optimisation Theory.
I look forward to connecting with you!
Some of the different themes I love working on.
The black-box approach of ML and AI might reinforce pervasive biases. I aspire to work towards AI/ML systems that are both powerful and fair, and interpretable.
Passionate about leveraging statistics and machine learning to solve problems from the domain of credit risk.
Unlocking powerful insights: Bayesian modeling empowers data-driven decision making with probabilistic reasoning and flexibility in complex systems.
Financial data almost always includes the temporal notion, and RNN, LSTM and other ML methods provide a fascinating lens to approach such problems.
I am interested in applying Optimisation and other Data Science techniques to build portfolios catering to custom goals.
In finance, uncertainty is the norm, and with UQ techniques, one can quantify these uncertainties and allow for a more accurate assessment of the potential risks and rewards associated with financial decisions.
Developing natural language processing (NLP) solutions that help analyze the effectiveness of risk controls implemented to mitigate risks associated with e-trading activities.
Analysing and Modelling various forms of credit risk, by leveraging Amex internal, US Credit Bureau data and other data vendors. Intensive use of Big Data tools like Spark and Hive, and A/B Testing to drive intelligent credit and collection decisions.
Leveraging Credit Bureau Tradeline data with AWS Sandbox for modelling Credit Risk volatility during Covid.
Improving the customer contact model for collections portfolio
Developing a unified suit for meta tagging and analytics of video platforms
Understanding the dynamics of future curves is crucial for traders and investors in the commodity markets. We have developed a predictive model using Eigen value decomposition and HMM-GMM to provide signals for shift in price structure of Brent futures, that can help inform decision-making.
Details Github