I am a Data Scientist with over 10 years of experience working at the intersection of data, machine learning, and real-world business problems. My background spans the full lifecycle of data science projects—from understanding ambiguous business questions and exploring data, to building scalable machine learning systems and deploying them into production environments
Over the years, I have worked extensively on predictive modeling, time-series forecasting, and statistical analysis to support decision-making in areas such as demand planning, customer behavior, risk modeling, and operational optimization. I have designed and maintained robust data pipelines, automated analytical workflows, and built models that are both accurate and practical to use at scale.
In recent years, my focus has increasingly shifted toward Generative AI and large language models. I have built LLM-powered applications including intelligent chatbots, retrieval-augmented generation (RAG) systems, semantic search solutions, natural-language-to-SQL tools, and end-to-end NLP pipelines. These systems are designed not just as experiments, but as production-ready tools that improve efficiency, reduce manual effort, and enhance user experience.
Beyond individual contribution, I have led and mentored teams of data scientists and engineers, collaborating closely with product, engineering, and business stakeholders. I place strong emphasis on clean design, measurable impact, and aligning technical solutions with business goals. I believe that good data science is not just about sophisticated models, but about building solutions that are reliable, interpretable, and valuable to the people who use them.
I enjoy teaching, mentoring, and sharing my learnings with the broader data science community. Through this website, I aim to share my work, notes, and experiments, and to help others build a strong foundation in applied data science and modern AI.
Siddharth