Shreyasree G

WORK HISTORY

EDUCATION

ABOUT SHREYASREE G

I build data-driven products at the intersection of biotechnology and machine learning, turning complex biological data into tools and insights that can actually be used.What I do best is take ambiguous, research-heavy problems and shape them into structured ML workflows: defining the right inputs, choosing pragmatic modeling approaches, and validating outputs against real biological and business constraints. I’ve learned that good ML products aren’t about the fanciest models; they’re about clarity, reproducibility, and decision-making impact.Along the way, I’ve picked up a few core lessons- Start with the question, not the model- Design features that reflect domain reality, not just statistical convenience- Treat validation as a product requirement, not a research afterthought- Build pipelines that others can understand, reuse, and scaleSome concrete proof of work- Designed and implemented end-to-end ML pipelines for mutation risk prediction and ligand screening- Applied clustering, descriptor-based similarity, and deep learning to datasets spanning hundreds to tens of thousands of biological entities- Translated research outputs into clean, explainable frameworks suitable for downstream use and publication- Balanced delivery across academics, certifications, hackathons, and a research-focused internship at a reputed institute, while maintaining a 9.25 CGPARight now, I’m focused on roles and projects where ML meets real-world biological decision-making: whether that’s in computational drug discovery, bioinformatics platforms, or applied ML teams working on life-science products.If you want to talk about building ML products in biotech, translating research into usable systems, or interdisciplinary tech careers, feel free to reach out.