Master of Science, Engineering Science - Data Science

Relevant coursework - Machine Learning, Computer Vision, Data Models and Query Language, Numerical Math for Computing and Data Science, Programming and Database Fundamentals for Data Science, Statistical Learning and Data Mining, Information Retrieval, Industrial Applications of Data Science.

Bachelor of Engineering in Computer Science

Data Scientist

• Researched Machine Learning, Natural Language Processing (NLP) and created a predictive model to analyze and solve data classification problems in the company.
• Performed data mining by manipulating and drawing insights from large unstructured datasets.
• Developed end-to-end Machine Learning prototypes and scaled them to run in production environments. Manual time-consuming tasks were decreased by at least 40%.
• Designed and worked on state-of-the-art Machine Learning algorithms: BERT-Transformers and LSTM (Long
short-term memory) networks, and RNN to build a data classification model.
• Assessed and improved the model\’s performance by interpreting outputs using Shapley values, achieving an increased accuracy of 93%.
• Built interactive streamlit dashboards and integrated it with model\’s training and validation pipelines.
• Deployed and managed docker container-based applications on AWS elastic container service.

Machine Learning Intern

• Led and steered a team of 5 to develop Machine Learning model that could predict whether a person is diagnosed with diabetes or not based on specific diagnostic measurements given in dataset.
• Constructed Machine Learning pipeline, which involved data cleaning, pre-processing, model selection, hyperparameter tuning, and prediction generation.
• Predictive modelling algorithms includes Support Vector Machines, K-Nearest Neighbors, Random Forest, and ensemble methods like XGBoost and AdaBoost.