Siddharth Sameer

I am a data and systems engineer with experience building large-scale pipelines and production systems in financial services. My work focuses on turning real-world data into reliable inputs for analytics, personalization, and machine learning. I’ve spent the last few years working on high-throughput data systems, optimizing distributed pipelines, and ensuring data correctness under scale. Along the way, I’ve developed a strong interest in quantitative finance and applying machine learning carefully to noisy, time-series data. I’m currently pursuing a Master’s in Computer Engineering at NYU and working on quantitative research at NYU Courant, where I’m exploring how fundamental data and ML can be used to study stock returns in a principled, out-of-sample way. I enjoy roles that sit at the intersection of engineering, data, and quantitative thinking — especially in environments that value correctness, clarity, and long-term reliability over quick wins.