Machine Learning Quant

Entec Partners; New York (NY); On-site; USD 140,000 - 230,000; Full time

Job summary

Entec Partners is a leading proprietary trading firm driving the future of intelligence in financial markets. The team develops ML/AI models for prediction, signal generation, and anomaly detection, and deploys them into live trading strategies with rigorous backtesting and monitoring.

The role involves collaborating with Quant Research and Trading Intelligence, exploring advanced techniques, and owning models end-to-end from research to production in a high-performance environment.

Qualifications

• PhD or MSc with significant ML/quantitative experience.
• Hands-on experience building and deploying ML/AI models.
• Experience with time series forecasting or anomaly detection.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Production-grade software development in a real-world environment.
• Experience with Docker, S3/MinIO, and PostgreSQL/OLAP databases.
• Strong understanding of overfitting, generalization, and feature engineering.
• Stays up to date on ML/AI literature and experiments with new ideas.
• Ability to communicate complex ideas clearly and collaborate in high-performance teams.

Responsibilities

• Develop, test, and deploy novel ML/AI models for prediction, signal generation, and anomaly detection.
• Work closely with the Quant Research and Trading Intelligence teams to translate insights into live strategies.
• Explore state-of-the-art techniques (e.g., deep learning, reinforcement learning, graph neural networks) and rigorously evaluate their applicability in financial domains.
• Take full ownership of your models and strategies - from signal research, feature engineering, and backtesting through to execution, live performance monitoring, risk assessment, and iterative improvement in production.
• Analyze large, noisy, high-frequency data streams; performing advanced feature engineering, and bias detection.

Skills

Python

PyTorch

TensorFlow

scikit-learn

Time series

Anomaly detection

Production deployment

Docker

S3/MinIO

PostgreSQL/OLAP

Communication

Collaboration

MLFlow

CUDA

Education

PhD in ML/CS/Statistics

MSc with significant experience

Tools

Docker

S3/MinIO

PostgreSQL/OLAP

CUDA

MLFlow

Job description

Leading proprietary trading firm is building the Future of Intelligence in the markets. Their teams of researchers, quants, engineers and traders collaborate to push the boundaries of what's possible. If you're passionate about machine learning, statistical modeling, and want your work to directly impact performance in real-world financial markets, you'll feel right at home here.

Responsibilities:

• Develop, test, and deploy novel ML/AI models for prediction, signal generation, and anomaly detection.
• Work closely with the Quant Research and Trading Intelligence teams to translate insights into live strategies.
• Explore state-of-the-art techniques (e.g., deep learning, reinforcement learning, graph neural networks) and rigorously evaluate their applicability in financial domains.
• Take full ownership of your models and strategies - from signal research, feature engineering, and backtesting through to execution, live performance monitoring, risk assessment, and iterative improvement in production.
• Analyze large, noisy, high-frequency data streams; performing advanced feature engineering, and bias detection.

Requirements:

• A strong academic background in Machine Learning, AI, Statistics, Computer Science, Mathematics, or a related field - typically a PhD or equivalent, or an MSc with significant relevant experience
• Hands on experience building and deploying ML/AI models, especially for time series forecasting or anomaly detection.
• Genuine curiosity about trading, market microstructure and financial dynamics
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Solid programming and software development skills, with experience in a production environment.
• Experience with core data and infrastructure tools like Docker, S3/MinIO, and PostgreSQL/OLAP databases.
• A deep, intuitive understanding of topics like overfitting, generalization, and feature engineering.
• Stays up to date on ML/AI literature and experiments with new ideas
• The ability to communicate complex ideas clearly and collaborate effectively in a high-performance team.

Preferred qualifications:

• MLOps pipelines and tools (e.g., MLFlow, ClearML, Weights & Biases).
• High-performance computing and GPU optimization (e.g., CUDA, TensorRT).