Machine Learning Researcher – PhD: 2027
A research role in the machine learning group, applying deep learning to forecasting problems such as time series and language. Models feed directly into systematic trading.
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.
- Develop deep learning models for trading signals across asset classes
- Design experiments to improve predictive power
- Turn research models into production-ready versions
- Monitor and refine live models with engineers and traders
What they ask for
Required
- PhD in CS, ML, maths, physics or statistics
- 5+ years building deep learning systems
- Top-conference publications
- Python and/or C++
- PyTorch or TensorFlow in production
- Deep learning on time series
- Education
- PhD in computer science, machine learning, mathematics, physics, statistics or a related field
- Experience
- 5+ years of applied deep learning in academia or industry
- Languages and tools
- Markets
- multi-asset
How to prepare
- Build a deep time-series forecaster and compare it with simple baselines
- Study overfitting and validation for financial data
- Practise moving a research model into reproducible code
- Read recent work on transformers for time series
What to study
- Financial Machine Learning
- Probability, Statistics and Financial Data
- Text as Data and NLP for Finance
- Econometrics and Financial Time Series
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 Susquehanna International Group (SIG)’s site before applying: requirements, location and pay can change after we check them.
View the posting on Susquehanna International Group (SIG)’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Susquehanna International Group (SIG) and doesn’t handle applications.