Data Scientist - IQVIA

Senior Machine Learning Engineer - Quantiphi Analytics

  1. Mentored and Spearheaded a team of 7 Machine Learning Interns to build a Proof of Concept.
  2. Developed a customer attrition model using a large dataset of 20 million records with machine learning algorithms such as XGBoost, Random Forest, CatBoost, and Artificial Neural Networks to predict customer churn based on historical data.
  3. Implemented and worked with Language Models such as PaLM, T5, BERT, and GPT. Built an open-source chat application that is capable of conversing with the user as well as documents.
  4. Focused on ideating, brainstorming, researching, and building innovative AI and ML products, solutions, or workflows.
  5. Developed a Custom Synthetic Document Generator from scratch that is used to generate synthetic documents that can be used for training ML models.
  6. Using synthetic data generated by CSDG, I trained a custom processor in Google Cloud Document AI and automated the document tagging process, which reduced the time and manual effort required to find training documents and document annotation by 50%.
  7. Worked on Document AI services in Google Cloud Platform (GCP) for document understanding, processing, extraction, and analysis.
  8. Conducted a hypothesis test to prove the correlation between prostate cancer incidence. Employed statistical approaches including z-test and p-value analysis to validate the hypothesis.
  9. Utilized Random Forest and XGBoost algorithms for predictive modeling to enhance the accuracy of our findings.
  10. Worked on a proof-of-concept to implement Sentiment Analysis using state-of-the-art Transformer models, including BERT, RoBERTa, DistilBERT, and XLNET