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DRV

Quantitative Data Scientist

Extreme Demand$130K – $350K+
Quantitative Data Scientists sit at the intersection of data science and quantitative finance. They bring modern ML techniques — XGBoost, neural networks, NLP, computer vision — to financial problems that traditional quants approach with classical statistics. This is the fastest-growing quant role because financial data is exploding in volume and variety. Satellite imagery, social media sentiment, credit card transactions, shipping data — all contain potentially tradable information. Quant data scientists build the pipelines to ingest, process, and extract signal from these diverse data sources.
8
Core Skills
10
Key Tools
10
Lessons
6
Career Stages

A Day in the Life

“You're building an LSTM-based exit model for the V7 engine. The current best model uses 30 features including bars_held, current_r, mfe_r, and mae_r. You hypothesize that adding an attention mechanism over the last 10 bars could improve exit timing by 5%. After training on 3 years of data with walk-forward validation, the attention model shows 61% exit accuracy vs 58% baseline. You package the model using ONNX for production deployment and write the A/B testing framework to validate live performance.”

Core Skills

1
Supervised / Unsupervised Learning
2
Deep Learning (LSTM, Transformers)
3
NLP for Finance
4
Feature Engineering
5
Model Selection & Tuning
6
Big Data Processing
7
Alternative Data Analysis
8
MLOps

Tools & Technologies

PythonTensorFlowPyTorchscikit-learnXGBoostLightGBMSparkSQLJupyterMLflow

Prerequisites

✓Machine Learning
✓Python / Data Stack
✓SQL
✓Statistics
✓Basic Finance

Career Progression

Data Scientist
→
Quant Data Scientist
→
Senior Quant DS
→
Lead ML Engineer
→
Head of ML
→
VP Data Science

Curriculum

1
SUP Supervised Learning for Trading
Intermediate • 50 min • Lesson 1 of 10
→
2
XGB XGBoost & LightGBM for Trading
Intermediate • 55 min • Lesson 2 of 10
→
3
NET Neural Networks for Finance
Advanced • 55 min • Lesson 3 of 10
→
4
SEQ LSTMs & Sequence Models
Advanced • 55 min • Lesson 4 of 10
→
5
FEA Feature Engineering for Trading
Intermediate • 70 min • Lesson 5 of 10
→
6
IMP Feature Importance & Selection
Advanced • 50 min • Lesson 6 of 10
→
7
SEL Model Selection & Validation
Advanced • 50 min • Lesson 7 of 10
→
8
ENS Ensemble Methods
Advanced • 50 min • Lesson 8 of 10
→
9
MLOP MLOps & Deployment
Advanced • 35 min • Lesson 9 of 10
→
10
ALT Alternative Data for Trading
Expert • 40 min • Lesson 10 of 10
→

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