Senior Data Scientist (Quantitative Finance & ML)
Skills
About This Role
Overview
1.
Time-Series Forecasting: Design and implement advanced machine learning models (e.g., LSTM, GRU, Transformers, or XGBoost) specifically optimized for non-stationary time-series data and stock price forecasting.
2.
End-to-End Pipeline Development: Build and maintain robust data pipelines for ingesting high-frequency market data, alternative data (sentiment, news), and fundamental indicators.
3.
Advanced Feature Engineering: Develop sophisticated features including technical indicators (RSI, MACD), volatility measures, and market microstructure signals to improve model alpha.
4.
Exploratory Data Analysis (EDA): Conduct deep-dive analysis to identify patterns, seasonalities, and anomalies in historical market data while accounting for "look-ahead" bias.
5.
Data Cleaning & Normalization: Handle complex data issues such as missing values, stock splits, dividends, and outliers that frequently corrupt financial datasets.
6.
Backtesting Frameworks: Create rigorous backtesting environments to evaluate model performance, ensuring results are statistically significant and account for transaction costs and slippage.
7.
Model Validation & Testing: Implement Walk-Forward Optimization and Cross-Validation techniques tailored for temporal data to prevent overfitting.
8.
Risk Management Integration: Incorporate risk metrics (Sharpe Ratio, Maximum Drawdown, Value at Risk) into the model evaluation process to ensure capital preservation.
9.
Deployment & Monitoring: Deploy models into production environments and set up real-time monitoring to detect "model drift" as market regimes change.
10.
Stakeholder Communication: Translate complex algorithmic results into actionable insights for portfolio managers and executives, bridging the gap between data science and finance.
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