I am a data scientist with over three years of experience, and the expertise to develop end-to-end data products that are, 1. High-performing (Feature Engineering, Hyperparameter tuning, and Cross-Validation) 2. Explainable & Interpretable (Explainable AI - Global & Local) 3. Making fair decisions (AI Fairness - Fairness Pre/In/Post-processing) 4. Deployable (CI/CD, Git, Azure, AWS) 5. Monitorable ( Model, Data drift, Fairness monitoring using Tableau, Plotly + Dash, Power BI, AWS Quicksight) 6. Efficient (Code complexity, Model training time, Time and space complexity) Furthermore, I devote my time to tutoring students in machine learning and data science utilizing a story-based learning approach. In addition, I publish research on topics related to machine learning and computational neuroscience and actively participate in Kaggle and online/offline hackathons. As the chairman of DSConnect, I organize technical meet-ups, expert talks, and social events.