🎓 Master's Student | Arizona State University (Class of 2026)
📈 Ex-Data Analyst | IIT Roorkee Alumnus | ML & Forecasting Enthusiast
📍 Tempe, Arizona | LinkedIn | GitHub
I’m a data scientist in training with a passion for turning real-world complexity into structured, actionable insights.
My journey began at IIT Roorkee, where I developed a strong foundation in analytical thinking. Since then, I’ve worked on pricing models, time series forecasting, and financial analytics at the enterprise level. These experiences taught me how to use data to solve high-stakes business problems.
Currently, I’m pursuing a master’s degree at Arizona State University. My focus is on machine learning, big data systems, and building personalized AI agents that adapt to human behavior. I enjoy solving problems that blend statistical modeling, practical intuition, and clean execution.
If it involves messy data, thoughtful modeling, and clear storytelling, I’m all in.
Fine-tuned LLMs (LLAMA2, LLAMA3.2, Mistral-7B) for personalized recipe generation using QLoRA. Achieved 96% precision and BLEU 0.52.
Built a collaborative filtering recommendation engine on the Book-Crossing dataset, leveraging Spark MLlib.
Analyzed Yelp data to uncover trends in restaurant performance and user behavior using Spark SQL.
- 🧠 Researching Personalized AI Agents using LLMs (ASU – under Prof. Ming Zhao)
- 🧮 Deepening my understanding of Machine Learning via applied projects and Udemy coursework
- ☁️ Learning cloud deployment tools (AWS/GCP) to scale ML models
- 🛠️ Enhancing Git and GitHub workflows for collaborative development
If you're working in Machine Learning, AI, or data-driven product development, I’m always up for meaningful conversations, idea exchanges, or research collaborations. I’m especially passionate about real-world applications of ML in forecasting, pricing, and personalization. Let’s connect and build something impactful together.
📧 Email: taljindergill78@gmail.com
🔗 LinkedIn: linkedin.com/in/taljinder-singh78
💻 GitHub: github.com/taljindergill78
