During my academic journey, I served as a Teaching Assistant for several advanced data science and machine learning courses, including Advanced Statistical Learning, Big Data Analytics, Statistical Learning for Big Data, and Probabilistic Reasoning for Machine Learning. In this role, I led exercise sessions, supported students with challenging concepts, and graded assignments—strengthening my ability to explain complex ML ideas clearly and practically.
Before and alongside academia, I built 5+ years of industry experience as a Software Engineer across software and industrial environments, with a strong focus on Python and production-grade development. I'm comfortable taking ideas from prototype to production using clean engineering practices, testing, and scalable design—and I've also contributed in consulting-style engagements across multiple domains, adapting quickly to different stakeholders, data realities, and business goals.
Across roles and projects, I've worked in diverse industries and research areas, including energy, consumer electronics, healthcare, and the broader software industry, as well as academic research such as gene expression data analysis, biomedical research, and ML-driven analytics. More recently, I completed an AI project on time-series anomaly scoring using LSTM models, applying an end-to-end workflow: data cleaning and integration, transformation and feature engineering, model training, evaluation, and clear visualization of results.
Resume
Github
Chandra Baidya
