Tejas Rastogi

About Tejas Rastogi

I am currently working as a Quantitative Research Consultant at WorldQuant, where I focus on systematic model development, alpha discovery, and data-driven investment research.My work involves designing and validating quantitative signals using statistical modeling, probability theory, and time-series analysis. I build structured research pipelines, develop robust backtesting frameworks, and conduct rigorous experimentation to evaluate strategy performance and risk-adjusted returns in institutional-style environments.Beyond quantitative research, I build and deploy full-stack systems that translate complex models into scalable, real-world applications. I have hands-on experience in backend architecture, API development, and integrating machine learning models into production-ready systems.My technical foundation is strengthened through C++ and Data Structures & Algorithms, sharpening core problem-solving depth while applying machine learning to research-driven challenges.I follow a disciplined, learning-by-building approach — analyze deeply, optimize continuously, and execute with clarity.Focused on quantitative research, ML systems, and scalable backend engineering

Experience

1. Quantitative Research Consultant

   WorldQuant

   Feb 2026 — Present · Greenwich, CT, US

   Secured this opportunity as a Quantitative Research Consultant at WorldQuant after rigorous self-study, competitive research performance, and continuous experimentation in quantitative modeling.Working on advanced quantitative model development focused on systematic trading, alpha discovery, and data-driven investment strategies. Building research pipelines that simulate real-world institutional trading environments.• Designing and validating quantitative signals using statistical modeling and financial data analysis• Developing and optimizing backtesting frameworks to evaluate strategy robustness and performance• Applying machine learning, probability theory, and time-series analysis to identify market inefficiencies• Conducting structured experimentation with strong emphasis on risk-adjusted returns and model stability• Translating complex financial datasets into scalable, production-oriented quantitative modelsOperating in a fast-paced global research ecosystem that demands analytical depth, disciplined execution, and continuous improvement.

Education

• KIET Group of Institutions

  Bachelor of Technology - BTech, Computer Science and engineering