I am a Data Scientist with a Master’s Degree in Data Science from Northeastern University, Boston, driven by a passion for MLOps, Large Language Models, and Time Series Forecasting. My expertise spans machine learning, natural language processing, and data science, refined through impactful roles at Universal Music Group and Flynn Group. I thrive on using data-driven insights to inform strategic decisions in product development, marketing, and business operations. My portfolio showcases diverse ML and NLP projects, including marketing mix models, an in-house Retrieval-Augmented Generation (RAG) system, and an e-commerce product pricing solution. I am committed to leveraging cutting-edge technologies to solve complex problems and deliver innovative solutions with global impact.

• Languages: Python, R, SQL, Java
• Frameworks: TensorFlow, PyTorch, Pandas, NumPy, Scikit-Learn, Fast AI, Hugging Face, Matplotlib, Seaborn, Streamlit, Beautiful Soup, PyTest, MyPy, Prophet, NeuralForecast, HyperOpt, Optuna
• Cloud Platforms: Azure, GCP, AWS
• MLOps Tools: MLflow, Airflow, Kubeflow, Docker, FastAPI, ELK Stack, DVC, CI/CD
• Data Management Visualization: SQL server, MySQL, MongoDB, NoSQL, BigQuery, PySpark, dbt, Tableau, Power BI, Excel, Git, D3.js, Observable

• Implementing state-of-the-art LLM architectures
• Developing robust time series based models for various industries
• Exploring advanced MLOps practices for efficient model deployment and monitoring

• Master of Science in Data Science - Northeastern University, Boston
• Bachelor of Technology in Computer Science - Vellore Institute of Technology, Vellore

• Golden Retriever- In House RAG System For Data Science Teams : Accurate, offline RAG system empowering data science teams with personalized, reference-rich outputs while seamlessly managing in-house packages in secure, internet-free environments.
• Price Alchemy : An innovative MLOps-driven project that combines NLP and tabular models to suggest optimal pricing for diverse products on Mercari, optimizing e-commerce pricing strategies.
• Wikipedia Traffic Forecasting : A cutting-edge project leveraging LSTM models to predict daily Wikipedia page traffic, revolutionizing content management and resource allocation in the digital realm.
• AI Story Generator : NLP-powered story generator that crafts unique narratives, blending human creativity with machine learning to inspire and augment the art of storytelling.
• Image Captioning using Flickr8K dataset : Next-gen Image Captioning system that generates rich captions from photos improving visual search and accessibility across platforms.
• Judging Books By Their Cover- A Multi-Label Genre Classification System : Multi-label classification system leveraging ULMFiT approach and AWD-LSTM model to decode book descriptions into precise genre combinations, supercharging e-commerce platforms with intelligent content organization.
• Machine Learning Micro-Project Repository : A comprehensive collection of 30+ micro-projects showcasing diverse machine learning algorithms and techniques, designed to accelerate learning and demonstrate real-world applications for aspiring ML engineers.

• K.R., Jothi, Mehul Jain, and Ankit Jain. “A New Deep Learning Approach Enhanced with Ensemble Learning for Accurate Intrusion Detection in IOT Networks.” Adhoc & Sensor Wireless Networks 54 (2022).
• Jain, Mehul, Manas Suryabhan Patil, and Chandra Mohan. “Extreme Gradient Boosting for Toxic Comment Classification.” Computational Methods and Data Engineering: Proceedings of ICCMDE 2021. Singapore: Springer Nature Singapore, 2022. 401-412.
• Jain, Mehul, et al. “Data Extraction And Sentimental Analysis From Twitter Using Web Scrapping.” International Journal of Engineering and Advanced Technology (2019): 109-119.

• 🥁 Amateur drummer and flutist
• 🧩 Passionate about solving complex puzzles through code
• 📚 Avid reader of sci-fi, philosophy and popular science books
• ✍️ Read my blogs at: Thoughts, Code and Mischief

Feel free to explore my repositories and reach out for collaborations or interesting discussions!