Summary
Overview
Work History
Education
Skills
Projects and Papers
Accomplishments
Certification
Timeline
Generic
Sai Sudeep A

Sai Sudeep A

Summary

Enthusiastic data engineer with a passion on technology implementations, with excellent research, technical, and problem-solving skills. Detail-oriented and able to learn new concepts quickly. Strong aptitude for identifying risks and implementing actionable initiatives to improve quality.

Overview

3
3
years of professional experience
1
1
Certification

Work History

Intern

JP Morgan Chase & Co.
Bangalore
07.2023 - 06.2024
  • Developed a model to predict or estimate the futuristic revenues using multiple data parameters. To test the accuracy, validity and adaptability of the model, I designed and developed an application using python itertools, filtered the data using scipy, transformed the data using numpy and generated the different combinations of test case data, which will be supplied to model and validate the fitness or accuracy of the model and significance of the variables. Here I have implemented the parallelization to increase the speed where test should validate millions of combination of data. Adjusted R-Square improved by 60%.
  • Part of design and development team which reads the purchase feed from customers and transforms data to financial institutions to proceed with the purchase. Here we have NLP parser exposed via API which does the parsing and transformation of data. I also automated the test on the API using RestAssured framework.
  • Involved in creating test criteria and testing credit fraud detection application based on a hybrid random forest-KNN classifier. Performed input data checks and preprocessing tests to ensure optimum model fit.
  • Designed a series of statistical tests to asses the model outputs produced on the test dataset. One such test was the ‘KS hypothesis test’ on the observed and predicted distribution. The series of tests were automated and the final model rating was updated via Rest API.

Machine Learning, Intern

Swecha.org (Non-Profit)
Hyderabad
06.2021 - 07.2021
  • Worked on building a hand gesture based navigation system for an agricultural cart. Used OpenCV and Google’s pre-trained mediapipe model to track and recognize 5 hand gestures that map to various cart functions.
  • Built a movie recommendation system using cosine distance and Tf-Idf vectorization of movie descriptions in python. Lemmatization technique was used to tokenize words in the input dataset.

Education

B.E. Engineering & MS Economics -

Birla Institute of Technology And Science, Pilani
Pilani
06-2024

Skills

  • Python, Java, R
  • Machine Learning (TensorFlow, SciKit Learn, Keras)
  • Git, Github
  • API Testing using RESTAssured, PostMan
  • Jira, TestSuite
  • AWS S3, Amazon Athena DB

Projects and Papers

  • Predicting Annual Yield of Pearl Millet Crop in Rajasthan– written under Dr. S. Routroy, the paper describes the techniques used to achieve a prediction power of 78% Adjusted R2 by training an Artificial Neural Network (ANN) on meteorological data obtained through an API call to the ESA’s Copernicus satellite database. Millet yield, rainfall and temperature data was scraped from IMD’s website using the selenium package. An XGBoost model was used for feature selection. Sklearn and Tensorflow python packages were used for data preprocessing, model training and calculating the score metrics.
  • Predicting Stock Market Volatility to build trading strategies– Used an hybrid LSTM-GARCH model to build a stock market volatility predictor to forecast 30-day volatility. The daily close data of BSE-SENSEX provided by the ‘yfinance’ API was used as an input to the GARCH model and the LSTM memory cells through the input layer. The predicted volatility was used to backtest option strategies which resulted in a CAGR of 40% over a period of 8 months.
  • Gold or Bitcoin? which is a better instrument for hedging against inflation– Used Error Correction Models to analyze the movement of Gold and Bitcoin and their responses to policy changes. Conducted Cointegration, heteroskedasticity and autocorrelation on the time series data to justify modelling choices.

Accomplishments

  • Recipient of National Talent Search Examination (NTSE) level 1 scholarship conducted by the national council of educational research and training (NCERT).
  • Ranked 97.5 percentile in JEE Mains Examination conducted by National Testing Agency.
  • All India Rank 515 among 3 lakh students in BITSAT Examination conducted by BITS Pilani.

Certification

  • Software testing workshop in BITS
  • Natural Language processing in python
  • Python for Financial Analysis

Timeline

Intern

JP Morgan Chase & Co.
07.2023 - 06.2024

Machine Learning, Intern

Swecha.org (Non-Profit)
06.2021 - 07.2021

B.E. Engineering & MS Economics -

Birla Institute of Technology And Science, Pilani

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Sai Sudeep A