DataCurve Contributor
I have hands-on experience contributing to AI training data through my work at DataCurve, a Y Combinator-backed (W24, $17.7M raised) competitive engineering platform with over 14,000 participants across 40+ countries. I solved and created 10+ vetted problems in numerical methods, machine learning, and algorithms, with each submission evaluated on correctness, code quality, and reasoning depth. I ranked in the top 1% globally by output-quality scoring. This work involved designing problems with clear evaluation criteria, providing verified solutions, and ensuring that challenge quality was high enough to meaningfully train and benchmark AI systems. I also contributed to open source AI tooling projects with 20k+ GitHub stars, including ScrapeGraphAI, an AI-powered scraping framework where I shipped a feature that was merged and released to stable. What sets me apart is the combination of strong mathematical foundations (Mathematics and Computing degree from BITS Pilani, 9.14/10 GPA, with coursework in Probability, Numerical Methods, Optimization, and Stochastic Processes) and deep Python proficiency (NumPy, Pandas, SciPy, PyTorch, Scikit-Learn). I have built and rigorously validated complex systems including a high-frequency trading simulator with walk-forward ML model benchmarking, and a real-time financial analytics pipeline deployed in Docker with FastAPI. My upcoming visiting research at the National University of Singapore's Department of Mathematics further deepens my expertise in applied mathematical modeling. I bring a strong eye for correctness, edge-case thinking, and clear documentation, all of which are essential for producing high-quality AI training data.