Sharvil Katariya
Software Engineer
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSharvil Katariya is a software engineer with 11 years of experience building ML-driven products and cloud-scale systems, currently working at Samsung Research Institute after impactful roles at Microsoft and Samsung Electronics. He blends data science and engineering expertise—having shipped recommendation platforms, privacy-preserving feedback pipelines, and real-time audience targeting features that drove 40% user growth—and has published research and filed patents from his work. His hands-on background spans IoT, mobile, full-stack web, and cloud architectures, with practical experience deploying LSTM time-series models and feature-engineered pipelines for stock price prediction. A former MS student and TA at Stony Brook, he pairs strong academic foundations in NLP and computer vision with a curiosity-driven, cross-disciplinary approach to solving production problems.
11 years of coding experience
5 years of employment as a software developer
High School, High School at Delhi Private School,DubaiBachelor’s Degree, Bachelor’s Degree at International Institute of Information TechnologyMaster of Science - MS, Master of Science - MS at Stony Brook University
Marathi, Hindi, English
Stackoverflow
987reputation
311kreached
9answers
6questions
mongodb
top-5%
Github Skills (30)
python10
pandas10
machine-learning10
rnn-model10
n10
data-preprocessing10
lstm10
keras9
data-analysis9
mongodb9
forecast8
forecasting8
algorithm7
algorithms7
data-structure7
Programming languages (9)
Github contributions (5)
scorpionhiccup/StockPricePrediction
Mar 2016 - Oct 2019
Stock Price Prediction using Machine Learning Techniques
Role in this project:
Data Scientist
Data ScientistContributions:38 commits, 18 pushes, 1 branch in 3 years 8 months
Contributions summary:Sharvil primarily contributed to the project by implementing and refining machine learning models for stock price prediction. They added scripts for data preprocessing, including feature engineering such as volatility and moving averages. Furthermore, the user integrated data from the S&P 500 index and external APIs to enrich the dataset, and implemented an LSTM model for time-series forecasting using the Keras library. The user's work demonstrates a focus on data manipulation, model building, and applying machine learning techniques within the context of stock price analysis.
forecastingpythonstock-price-forecastingstock-price-predictionstock
scorpionhiccup/SearchEngineWikipedia
Jan 2016 - Dec 2019
Search Engine for Wikipedia Articles
Contributions:4 commits, 2 PRs, 2 pushes in 3 years 11 months
search-enginesearchwikipedia
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