1. Data Scientist (Programmer Analyst) at Cognizant Technology Solutions

(July 2019 – May 2021)

  • Explored and implemented Deep Learning techniques in the field of Natural Language Processing and Computer Vision
  • Implemented end-to-end Machine Learning/Deep Learning processes such as Data Preprocessing, Exploratory Data Analysis, Data Visualization, Feature Engineering, Model Building, Evaluation and Model Deployment

      Natural Language Processing

    1. Text Summarization
      • Built different iterations of Hugging Face transformers for abstractive and extractive text summarization
      • Human summarization comparison was done to confirm the model abilities
      • Libraries: Hugging Face, Spacy, Pandas, Streamlit, NLTK
    2. Rasa Chatbot
      • Presented and developed Rasa conversational chatbot for FAQ use-case and highlighted advantages of Rasa X for conversation-driven development to improve efficiency by ~1.5 times
      • Contributed to enhancing Rasa chatbot into multi-lingual chatbot to converse and respond with users in 6 European languages
      • Libraries: Rasa, RasaX

    Computer Vision

    1. Optical Character Recognition
      • Developed Optical Character Recognition as a service on hand-written and printed text in a tabular format using YOLO, Python tesseract and OpenCV
      • Deployed using Mlflow
      • Libraries: OpenCV, YOLO, Tensorflow, Mlflow

    Classification

    1. Multi-Class Classification
      • Experimentation and iteration to improve multi-class support ticket classification using data augmentation techniques and machine learning models such as Linear SVM, Multinomial Naive Bayes, Logistic Regression, Random Forest, and XGBoost
      • Libraries: Sci-kit-Learn, Pandas, Numpy, Matpotlib

    2. Data Science Intern at Cognizant Technology Solutions

    (Jan. 2019 - Apr. 2019)

      Time-Series

    • Developed revenue forecasting case study for a European telecom giant using ARIMA time-series model
    • Achieved an overall accuracy of ~91% with confidence interval of 95% in predicting revenue for three month rolling period
    • Libraries and tools: Pandas, Numpy, Matplotlib, Python, Jupyter Notebooks, Excel