{"entity":"person","person":{"name":"Muhammad Shoaib","role":null,"level":"Senior","country":null,"summary":"Quantitative finance researcher and applied statistics engineer (senior level) with demonstrated strengths in careful econometric inference and reproducible benchmarking for portfolio performance; the strongest proven skill is rigorous cross-sectional and time-series inference implemented in analysis/latent_performance.py and the HalfNormal SFA implementation in sfa/sfa_halfnormal.py. The codebase shows comprehensive unit and integration tests, deterministic report generation, and a deployable Streamlit/FastAPI MVP, but it lacks GPU/large-scale training, advanced MLOps automation, and explicit profiling or quantization artifacts in public code. 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