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Shaneeza Hasnani

I came to data science from fraud examination. I build models that understand why anomalies exist, not just that they do.

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About

Who I Am

SH
0 Years of Experience
0 Projects Completed
0 Internships
0 Certs & Awards
Skills

Toolkit

Languages

Python R SQL SAS Scala

Analytics & ML

Fraud Detection Machine Learning Anomaly Detection scikit-learn XGBoost Financial Forensics

Tools & Cloud

Power BI Tableau AWS Snowflake dbt Git
Projects

Featured Work

FEATURED · Event Study
Python Pandas Finance

Fraud Disclosure Event Study

I wanted to know how markets actually respond when corporate fraud goes public. Using the Fama-French Three-Factor Model, I measured abnormal returns around disclosure dates and found patterns most people overlook.

Fama-French r² = 0.87 · CAR significant at p<0.01
GitHub ↗
Machine Learning
Python Scikit-learn

Titanic Survival Prediction

A classic dataset, but my focus was on doing it right. Built a full preprocessing pipeline, engineered features from raw passenger data, and evaluated with accuracy, AUC, and F1 to avoid misleading metrics.

Accuracy: 82% · AUC: 0.89 · F1: 0.81
GitHub ↗
Data Analysis
R ggplot2 EDA

WMATA Ridership Analysis

Pulled 5 years of DC metro data and broke down ridership patterns by station, season, and whether it was a holiday. Found things that genuinely surprised me about how commuter behavior shifts.

5-year dataset · R² = 0.93 · Seasonal decomposition
GitHub ↗
Statistical Analysis
R Regression

NYC Job Salary & Career Analysis

Scraped and modeled NYC job posting salaries to understand what career level actually explains. Multiple regression with hypothesis testing on 2,400+ postings. The results were less obvious than you'd expect.

Adj. R² = 0.71 · n=2,400 jobs · p<0.001
GitHub ↗
Labor Analytics
R Data Mining

LinkedIn Skills & Salary Analytics

Used k-means clustering on LinkedIn job data to find which skill combinations actually drive salary. A personal project that started as curiosity about my own career path.

k=5 clusters · Silhouette = 0.68
GitHub ↗
Regression Analysis
R Modeling

Spotify Audio Features Analysis

Regression analysis on 5,000 Spotify tracks to see how much audio features like tempo, valence, and genre actually explain energy levels. Turns out acousticness is more predictive than genre.

RMSE = 0.09 · 5k tracks · R² = 0.76
GitHub ↗
Timeline

Career Path

2026 Education

M.S. Business Analytics & AI

American University · Kogod School of Business

Washington, DC · Machine Learning, Predictive Analytics, Financial Modeling, Data-Driven Decision Making

2026 Work

Research Assistant

Special Competitive Studies Project (SCSP)

2025 Education

B.S. Fraud Examination & Financial Forensics

John Jay College of Criminal Justice, CUNY

New York, NY · Fraud Detection, Financial Crime Investigation, Forensic Accounting, Data Analysis

2025 Certification

Certified Fraud Examiner (CFE)

Association of Certified Fraud Examiners

Passed all four parts of the CFE exam in August 2025. One of the most rigorous credentials in financial crime, and genuinely hard to earn.

2024 Work

Financial Crime Intern

Guidehouse

2024 Work

Fraud Data Analyst

EduGuide Overseas Pvt. Ltd.

2023 Work

Fraud Audit Intern

NY Attorney General's Office

2023 Work

Data Analyst

EduGuide Overseas Pvt. Ltd.

2021 Work

Data Analyst Intern

EduGuide Overseas Pvt. Ltd.

Contact

Get in Touch

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  "name":          "Shaneeza Hasnani",
  "status":        "available_for_work",
  "looking_for":  "Data Science · Fraud Analytics · ML roles",
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Email hasnani.shaneeza@gmail.com
LinkedIn linkedin.com/in/shasnani
GitHub github.com/shaneeza-hasnani