[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL]

PROFESSIONAL SUMMARY
Finance Data Scientist with [X] years building predictive models for trading, risk management, and fraud detection. Expert in time series forecasting, anomaly detection, and financial modeling. Strong understanding of financial markets, regulatory requirements, and quantitative analysis.

TECHNICAL SKILLS
Finance Domain: Quantitative Analysis, Risk Modeling, Portfolio Optimization, Algorithmic Trading, Credit Scoring
Programming: Python, R, SQL, C++
ML/Statistics: Time Series (ARIMA, GARCH), Random Forest, XGBoost, Neural Networks, Monte Carlo Simulation
Financial Tools: Bloomberg Terminal, FactSet, Refinitiv Eikon, Quandl
Libraries: pandas, NumPy, statsmodels, scikit-learn, TensorFlow, QuantLib
Databases: SQL Server, PostgreSQL, MongoDB, TimescaleDB

PROFESSIONAL EXPERIENCE

Data Scientist, Financial Services
[Financial Institution Name], [City, State]
[Start Date] – Present
• Developed fraud detection model processing [X]M transactions daily with [Y%] precision and [Z%] recall
• Built credit risk model improving default prediction by [X%] over existing system
• Created algorithmic trading strategy generating [Y%] annual return with [Sharpe ratio]
• Implemented portfolio optimization model managing $[amount]M in assets
• Reduced false positive rate in fraud detection by [X%] saving $[amount]M in operational costs
• Collaborated with compliance team ensuring model adherence to regulatory requirements

Quantitative Analyst / Data Scientist
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Built time series forecasting models predicting market movements with [X%] accuracy
• Developed customer lifetime value model for retail banking products
• Created churn prediction system identifying at-risk clients with [Y%] precision
• Implemented anomaly detection for transaction monitoring reducing false alerts by [Z%]
• Conducted stress testing and scenario analysis for risk management

KEY PROJECTS

Fraud Detection System
• Built ensemble model detecting fraudulent transactions with [X%] precision
• Processes [Y]M transactions daily with [Z] ms latency
• Reduced fraud losses by $[amount]M annually while minimizing customer friction

Credit Scoring Model
• Developed alternative credit scoring using non-traditional data sources
• Improved approval rates by [X%] while maintaining [Y%] default rate
• Compliant with fair lending regulations and model risk management requirements

Algorithmic Trading Strategy
• Created quantitative strategy based on [factors/signals]
• Backtested on [X] years of historical data
• Achieved [Sharpe ratio] with [max drawdown]

Market Risk Model
• Built VaR and CVaR models for portfolio risk assessment
• Incorporated [X] risk factors and [Y] scenarios
• Enabled risk-adjusted decision making for $[amount]M portfolio

EDUCATION
[Master's or Bachelor's], [Finance, Financial Engineering, Economics, Mathematics]
[University Name], [City, State]
Graduation: [Year]

CERTIFICATIONS
• [CFA Level I/II/III], [Year]
• [FRM], [Year]
• [Certificate in Quantitative Finance (CQF)], [Year]

DOMAIN EXPERTISE
• Financial Markets: Equities, fixed income, derivatives, commodities, FX
• Risk Management: Credit risk, market risk, operational risk, model risk
• Regulations: Basel III, Dodd-Frank, FCRA, ECOA, model validation standards
• Financial Metrics: Sharpe ratio, alpha, beta, VaR, information ratio