I’m Yash Sethi — a data science practitioner with a foundation in mathematics, a mind wired for problem-solving, and a genuine passion for uncovering insight in complexity.

My journey into data didn’t start with a course or a bootcamp — it began with equations, theorems, and a quiet curiosity cultivated during my Bachelors and Masters in Mathematics, with a minor in Statistics. Through those years, I learned how to think structurally, reason rigorously, and code with purpose — skills that later became the bedrock of my career in data science.

I took my first steps into the field at HSBC, where I worked across two distinctly different domains: marketing analytics and fraud mitigation. These experiences didn’t just give me technical depth — they offered perspective. I learned how data can drive campaigns one day and protect customers from fraud the next. I became fluent in building models that didn’t just predict, but made a measurable difference in business outcomes.

To expand my capabilities, I pursued a Master of Management in Analytics at McGill University, where I explored the full breadth of the data science lifecycle — from data engineering and ML modeling to dashboarding and deploying AI agents. During the program, I worked as a consultant for BNP Paribas, ALDO, and L’Oréal, solving diverse business challenges using advanced analytics and machine learning.

Today, I specialize in building end-to-end AI-powered solutions, especially those involving LLMs, Machine Learning architectures, and intelligent visualizations.

⚡ Fun Fact: When I’m not working with data, I’m usually go for a run, solve puzzles, learn French 🇫🇷, or strum ukulele I’ve been teaching myself for over 1300 days.

If you’re working on a data-driven problem — or even just want to chat about the future of AI — let’s connect.

• Machine Learning and Data Science: ANN, Deep Learning (RNN, CNN), NLP(Sentiment Analysis, TF-IDF, Word2Vec, BERT), Transformers(BERT), Regression, Classifiers, Random Forest, Gradient Boosting(XGBoost, LightGBM), Clustering (K-Means, DBSCAN)
• Analytics Tools & Software: SQL, Python, R, Power BI, Metabase, VSCode
• Python Libraries: Pandas, NumPy, scikit-learn, PyTorch, XGBoost, NLTK, Gurobi, Statsmodels, Matplotlib, Seaborn, Causalml, dowhy, requests, BeautifulSoup, torch
• Cloud: AWS, Azure, GCP
• Visualization: Looker, Tableau, PowerBI, Adobe Analytics

Microsoft Certified Azure Data Fundamentals (DP-900), Microsoft Certified Data Scientist Associate (DP-100)