I am graduate student at State University of New York, Binghamton. I love problem-solving and I thrive for continuous improvement. I like to lead people and explore new/unfamiliar technology. I have a strong analytical background with expertise in data science and machine learning.
In my past, I have worked with a variety of technologies majorly related to data and database. I have experience in backend production servers(maintenance and scalability of both relational & non-relational databases), manipulating large data and algorithm optimizations. I also have experience in data modelling and data visualization for small scale industries.
With passion for algorithms and data, I am actively looking for opportunities in Software Development and/or Data Science.
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Experience
Intern, M19 Material Intelligence Lab May 2020 – August 2020
• Increased site response score from 38 to 50 measured on Google Lighthouse by removing render blocking JS, minifying JS, HTML/CSS and reducing critical request chains in network payloads per request
• Planned & produced full stack web app for Business Analysis with React (JavaScript), Django (Python) and SQL
• Furnished static and interactive data visualizations in-browser for testing results of materials using Google Charts API
Technology Engineer, Amdocs Development Centre India LLP December 2018 – July 2019
• Reduced ticket processing time from 18 minutes to 3 minutes by integrating Linux server terminal as a dedicated chatbot in internal chatrooms of institution with the help of Python, NLTK and Keras (TensorFlow)
• Improved uptime of servers by approx. 1% by automating restarts of hung JVM’s measured in active hours per day
• Upgraded alerting system from shell to python 3.0 for payment critical applications for AT&T
Software Developer Intern, VST Infotech August 2017 – December 2018
• Designed & delivered an Android (Java) application to provide a tailored real time financial advice to entrepreneurs
• Predicted trends over inadequate financial history of 3 months by dynamically modifying weights of features based on input data size and available features for Random Forest Algorithm with accuracy of 89% and precision of 83%
• Conducted analysis of potential tree overfitting issues in Random forest by correlating features with number of trees
• Demonstrated growth of 4% (INR 15000/- approx.) on net profit after recommended advice in effect over 3 months
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Projects
Understanding US elections November 2020
• Studied US elections 2020 with respect to 2017 census to find out the correlations between different factors such as race and income strata to understand voting trends of US voters using MongoDB, and Jupyter notebook
• Rendered interactive GUI's to interpret these correlations using plotly graphical library
COVID-19 Tracker April 2020 – May 2020
• Created a Python app to track & predict growth of Novel COVID-19 virus by utilizing pandas, Facebook’s prophet API
• Designed desktop UI, implementing map features to view country-specific covid-19 cases on map using plotly
• Built geoprocessing model to view results through constantly updating map of world displaying cases per country
• Achieved an error rate of 2.75% for predictions over daily seasonality
Australian Wildfire Estimator December 2019 - March 2020
• Crafted Python tool to estimate the spread of wildfires based on heat and light radiation readings from satellites
• Studied wildfires by applying regression techniques on radiation signatures to accurately gauge wildfire growth with
root mean squared error less than 0.4
• Plotted interactive graphs to give insights and simulated the wildfires on map by utilizing the heat signatures
Robocon 2018 August 2017 – March 2018
• Formed and led team of 40 students; conquered 29th rank out of 170 teams contending in National Robocon 2018
• Formulated dynamic programming algorithm in C++ to navigate robot in 5 directions to decrease task time by 1.16%
• Minimized error in trajectory of a projectile using Mean Shift Clustering in C++ to detect hit with confidence map