M.Sc. Statistics & Computing | Data Scientist and Analyst | Statistical Decision Making & Quant Researcher | Applied AI Scientist
I specialize in building high-throughput data pipelines, autonomous multi-agent AI systems, and quantitative research engines. My target is to extract actionable signals from complex datasets through rigorous statistical modeling, quantitative research, and applied artificial intelligence by focusing on mathematical depth, deep vectorization, and executing large-scale data analysis.
- Quantitative Systems & Backtesting: Developing backtesting automation for Alpha signals, state detection, and Sharpe ratio optimization. Processed 1M+ mutual fund NAV records using vectorized Python.
- Large-Scale Statistical Processing: Architected pipelines handling 41GB / 15B+ Wikimedia pageviews efficiently using DuckDB.
- Applied AI & Decision Science: Designing autonomous agentic business intelligence workflows driven by LLMs for automated executive reporting with proper failover mechanisms.
- Problem Solving and Decision Making: Solving large-scale analytical problems through structured reasoning, hypothesis-driven investigation, statistical validation, and efficient resource-aware system design while balancing accuracy, robustness, and business impact.
- Mastering advanced time-series fundamentals, statistical concepts for business problems, business metrics understanding, and advanced AI modeling.
- Developing skills in production-grade setups, AI engineering, statistical evaluation ecosystems, and engines for the autonomous and rigorous testing of results.
- Getting trained on real-world problems and high-volume data handling.