Quantitative Financial Engineer & Data Analyst

Namann Bhan

New York, NY P: +1-917-***-**** ******@***.*** LeetCode GitHub

EDUCATION

New York University New York, NY

Master of Science in Financial Engineering May 2026

• Cumulative GPA: 3.6/4.0

• Teaching Assistant: Fixed Income Algo Trading (Spring 2026)

• Clubs/Activities: Poker, Chess, Soccer

• Relevant Coursework: Stochastic Calculus, Brownian Motion, Ito’s Lemma, Numerical PDE’s [Feynman-Kac], Advanced Deep Learning [GARCH,LoRa], Market Microstructure, Quantitative Methods, Commodity Markets NMIMS University Mumbai, IND

Bachelor of Technology in Data Science Jul 2019 - Jul 2023

• Cumulative GPA: 3.65/4.0

• Relevant Coursework: Feature Engineering, Probability, Machine Learning, Linear Algebra, Time Series Analysis, Data Driven Modeling, Data Structures, Deep Neural Networks, Applied Statistics, Cross Validation TECHNICAL SKILLS / LICENSES

Languages: Python, SQL, kdb+/q

Tools and Frameworks: PyKx, NumPy, Pandas, QuandL, SnowFlake, TensorFlow, Keras License: Series 7 Candidate [SIE Passed]

CME Futures Trading Challenge [Rank: 248/2689, top 10%] Rank #1 of 17 interns in the Ensono Summer Associate Challenge WORK EXPERIENCE

Ensono (New York) May 2025 - Jan 2026

Financial Analyst

• Designed and developed an end-to-end data validation and reconciliation pipeline in Python (Snowflake, Regex) to systematically verify 75000+ contractual data points against internal records, improving data integrity alongside cutting processing time from 5 hrs to ~ 10s.

• Implemented a regression-based pricing model for fair mainframe valuation using historical transaction and quantitative data; performed model diagnostics ((R = 0.91, residual analysis, overfitting checks) to validate predictive stability and support data-driven vendor negotiations (~18% cost reduction). Lifestyle International (Mumbai) Aug 2022 - Jan 2023 Investment Analyst

• Engineered a time-series forecasting framework in Python to model sentiment-driven revenue; incorporated cross validation and back testing techniques to ensure robustness and reduce forecasting error by 26%.

• Architected a data pipeline integrating real-time APIs (Twitter, TikTok) and NLP models, improved data quality checks and preprocessing validation for 5M+ data points. Protiviti (Mumbai) May 2022 - Aug 2022

Machine Learning Engineer

• Constructed NER-based text extraction models (SpaCy) to identify key candidate attributes from unstructured resumes, achieving 98.2% accuracy and cutting data-prep time by 30%.

• Enhanced Logistic Regression and Cross Validation (GridSearch) to model drop out probability, achieved AUC=0.88, thereby improving HR’s resources efficiency considerably by ~15% and ensuring model robustness alongside reducing overfitting on sparse data.

PROJECTS

MARKET OVERNIGHT ANOMALY GitHub

• Implemented a research paper analyzing overnight return strategies via BSI, VIX and SPX trends, demonstrating higher returns/vol during night sessions vs intra-day trading GREEKS SIMULATOR GitHub

• Deployed a BSM option pricing model via Streamlit to dynamically visualize the behavior of Greeks under different market conditions.

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