Quantitative researcher

Adele Shahi

New York, NY 917-***-**** *****.*****@*****.***

PROFESSIONAL EXPERIENCE

Proof Trading — Quantitative Researcher Nov 2023 – Present

• Designed and benchmarked panel data models (GLM, GEE, GLS,) to forecast close auction sizes across 2 years of U.S. equity data, combining model stacking with decision tree residual modeling to reduce forecast error from 0.35 to 0.10.

• Segmented firms by industry category to detect structural similarities and separate firm-level from sector-level auction patterns, enhancing model robustness and interpretability.

• Developed ETF-to-symbol proxy models using Lasso regression with inverse notional value weighting to improve predictive signal extraction under heteroskedastic conditions.

• Applied Group K-Fold cross-validation to ensure generalization of forecasting models across time and symbols, reducing proxy model error from 0.0010 to 0.0006.

• Implemented changepoint detection and volatility segmentation (Fano factor, rolling correlation) to model regime shifts and abnormal price movement zones.

• Co-authored internal white paper on forecasting methodology and contributed to forthcoming academic publications. Research Foundation of CUNY (RFCUNY) — ERP & Financial Data Analyst 2022 – 2023

• Engineered relational databases and ETL pipelines in Claris FileMaker and SQL to clean, transform, and centralize messy grant data tied to research projects, funding cycles, and space utilization.

• Built Power BI dashboards to monitor indirect cost recovery, grant distributions, and lab usage KPIs across departments, enabling budget allocation decisions by senior leadership.

• Designed Balanced Scorecard-aligned strategy maps linking departmental metrics (e.g., $ per sq ft, funding per student) to institutional goals, improved transparency in performance tracking.

• Translated compliance constraints (e.g., deliverables per grant stage) into analytic triggers for real-time grant status updates and early identification of risk or rejection flags. FANAP Co. — Senior ERP & Strategy Analyst 2015 – 2021

• Spearheaded full-cycle ERP analytics across 6+ functional areas (Finance, HR, SCM, Inventory); translated multi-level workflows into KPIs, ETL pipelines, and dashboard-ready datasets.

• Led cross-departmental KPI design and strategy mapping for FANAP’s flagship ERP product (MIC Solution); integrated Balanced Scorecard methodology into executive reporting.

• Managed analytics projects for 50+ B2B clients in fintech, health, energy, and tourism; advised startups on market-entry strategies using predictive modeling and industry benchmarking.

• Formed and directed COVID-19 analytics task force; deployed internal mobile app with live alerting and health-risk scoring for 8,000+ employees across 20+ business units. EDUCATION

M.Sc., Data Science Engineering City University of New York 2023 Capstone: Vision-based navigation Optimization (Unav System for the Blind) M.Sc., System Management & Productivity Tehran Polytechnic 2015 Thesis: Multi-response robust design for Manufacturing strategy B.Sc., Statistics Isfahan University of Technology 2009 CERTIFICATIONS

- Tableau, Power BI, Claris FileMaker 2023

- Data Science & ML, Khatam University 2019

SELECTED PUBLICATIONS

- Closer to Size: Predictability of U.S. equity auction sizes 2024

- Pulse Propagation in Nonlinear Helmholtz Systems, Applied mathematics 2023

- Churn Prediction via Meta-Classifier –Banking Sector 2019

- ERP Strategy Design under Uncertainty – Government Case 2016 SKILLS & INTERESTS

Programming & Tools: Python (Pandas, NumPy, SciPy, Sklearn, Statsmodels), SQL, R, SAS, Stata, Git, Jupyter Modeling & Analysis: Time Series (ARIMA), Panel Data (GLS, GEE, FE/RE), Decision Trees, Monte Carlo Simulation Market Microstructure: Close Auction Modeling, ETF-Symbol Matching, Changepoint Detection Business Intelligence: Power BI, Tableau, Claris FileMaker, Oracle BI Mathematics & Methods: Linear Algebra, Probability, Robust Regression, Causal Inference (DiD, IV, RDD)

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