About Me
I am a data science enthusiast and an active data science mentor with Masters degree in Computer Science from University of Utah.
I was a PhD candidate for a brief period of Spring 2017 to Summer 2019.
My work included deploying solutions for time-series and forecasting problems, data analytics and performance optimization for software verification tools.
I like to read books, research papers and articles on Machine Learning. I also participate in Kaggle discusions whenever possible.
Experience
(Senior) Data Scientist, Arivo Acceptance LLC
- 35% increase in market capture rate by collaborating on updating the existing risk model to evaluate risk of default associated with new auto loan applications.
- 65% reduction in cost to verify income from credit bureau by building and deploying an income predictor for sub-prime customers for auto loan applications.
- Reduced customer deliquency rate by designing a linear programming optimization algorithm to help with portfolio management at collections department.
Data Science Mentor (MIT-IDSS), Great Learning
- Mentored over 300 students in collaboration with MIT for Applied Data Science Program (ADSP) and Data Science and Machine Learning (DSML) program.
- Instructed 60+ cohorts with an average feedback rating of 4.7/5.
- Topics include Hypothesis Testing, Regression, Classification, Deep Learning, Recommender Systems, Time Series Forecasting, Graph Neural Networks, AutoML tools.
Data Scientist, Aakash 88 LLC
- Increased annual profits by 13% by deploying time series models to forecast hourly load capacity and energy price for 150 wind farms in Texas to perform energy trading.
- Reduced manual analysis time by 35% through automation.
- Increased the volume of daily trades by monitoring and updating features in real time.
Machine Learning Researcher, University of Utah (SOARlab)
- Worked with SoarLab group under Prof. Zvonimir Rakamaric
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Given a program and set of properties, the objective was to use SMACK
to provide satisfiability (Memory overflow, Concurrentcy, Memory safety, etc) and provide evidence for the satisfiability condition under 900s.
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I implemented an MLOps solution to check the satisfiability of the properties and implemented a subspace clustering and local search optimization to speed-up
SMACK to reduce TIMEOUT errors. ALthough the focus was mainly to reduce the TIMEOUT errors, the implementation helped reduce Type II errors across all
SV-COMP categories.
Graduate Research Assistant, University of Utah
- Prof. Suresh Venkatasubramanian (Summer 2014, Summer 2015)
- Prof. Vivek Srikumar (Summer 2016, Fall 2016)
Graduate Teaching Assistant,
University of Utah
- Courses included Advanced Algorithms (Fall 2014, 2015), Data Mining (Spring 2015) and Discrete Mathematics (Spring 2016, Spring 2017).