Quantitative Researcher – Machine Learning
A machine-learning researcher who applies modern models, including transformer-style architectures, to forecasting problems in a trading firm. Strong ML people from tech or academia are considered even without finance experience.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Design and train ML models for forecasting tasks
- Build production-grade ML pipelines
- Extract signal from large noisy structured and unstructured datasets
- Tune architectures, features and hyperparameters for robustness
What they ask for
Required
- PhD (or very strong MSc) in ML, computer science or similar
- Deep theory and practice of core ML algorithms
- Feature engineering and hyperparameter tuning
- Track record taking models from research to production
Preferred
- Experience applying ML to financial markets
- Education
- PhD, or exceptional MSc, in machine learning, computer science or a related field
- Experience
- Proven record of deploying ML models to production
How to prepare
- Reproduce a modern sequence model (e.g. transformer) on a noisy time-series task and report honest out-of-sample results
- Practise handling low signal-to-noise data and avoiding overfitting
- Build a small end-to-end ML pipeline with monitoring
- Read how ML is used in finance, including its limits
What to study
- Financial Machine Learning
- Python for Quantitative Finance
- Probability, Statistics and Financial Data
- Text as Data and NLP for Finance
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
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Read the full posting on DRW’s site before applying: requirements, location and pay can change after we check them.
View the posting on DRW’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with DRW and doesn’t handle applications.