Satyam Raj

Experience

• 

  Tata Consultancy Services

  May 2018 - Jun 2018

  Summer Internship

  • Revamped a VBScript program into Java, fortifying it against errors and crafting an automated testing system for enhanced reliability.

• 

  Goibibo

  Jun 2019 - Jul 2019

  Technology Intern (Machine Learning)

  • Engineered and deployed a TF-IDF and Naive Bayes Classifier-based sentiment analysis model on customer reviews, attaining an impressive 85% accuracy in identifying negative feedback.• Spearheaded a proactive approach that yielded a substantial 30% reduction in customer complaints and a remarkable increase in positive customer sentiment.

• 

  Accenture

  Sept 2020 - Jul 2022

  Software Engineer - Machine Learning

  ● Developed and deployed a robust regression model using Random Forest Regression on AWS to predict invoice payment dates, achieving a low Mean Absolute Error (MAE) of 2.2 days, representing a 25% improvement over the previous system.● Developed a Named Entity Recognition (NER) system to extract medications from patient records, reducing data entry efforts by 70% and decreasing medication errors by 25%.● Automated the classification of protein structures using a CNN-based ResNet50 model, expediting COVID-19 vaccine research by 30%.● Developed and deployed an end-to-end classification product using XGBoost with Optuna for hyperparameter tuning and threshold optimization, achieving a real-time prediction accuracy of 92.5%, all within a tight 4-months timeline. Show less

• 

  Arizona State University

  Oct 2022 - Mar 2023

  Research Aide - Machine Learning

  ● Developed a classification system using Artificial Neural Network algorithm to forecast financial loss reversals and identifying key financial factors to guide strategic decisions, achieving an accuracy of 92%.● Built a system using Gradient Boosting Regression algorithm to predict next-quarter earnings by strategically incorporating current quarter losses as a key predictive indicator, achieving an R-squared value of 0.90.

• 

  Arizona State University

  May 2023 - May 2024

  Developed a SAM Med 3D-based system by integrating oral-anatomical knowledge with data-driven design to address challenges in dental CBCT segmentation and lesion detection, achieving performance with a Dice Coefficient (DICE) score of 0.87 in segmentation. ● Developed a Speaker Diarization and Speaker Verification-based system to enable hearing aids to precisely amplify selective sounds, achieving a Diarization Error Rate (DER) of 20%.

  • Research Aide - Machine Learning

    Jan 2024 - May 2024

  • Research Aide - Machine Learning

    May 2023 - Jan 2024
• 

  Neuroscale AI

  Jul 2024 - Nov 2024

  Founding Machine Learning Engineer (LLM)
• 

  TARIY

  Dec 2024 - Feb 2025

  Machine Learning Engineer
• 

  Neubus

  Feb 2025 - now

  Machine Learning Engineer (LLM)