Machine Learning Researcher
A research role in the machine-learning group applying deep learning to forecasting problems such as time series and language. The work feeds directly into trading strategies.
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 time-series and language forecasting
- Run experiments on features, architectures and tuning
- Build automation that speeds up research
- Work with engineers to test and deploy models
What they ask for
Required
- Deep learning for time series
- PyTorch or TensorFlow
- Python and/or C++
- Mathematics, statistics and algorithm design
- Research publications at top venues
- 5+ years of impactful ML work
- Education
- PhD in computer science, machine learning, maths, physics, statistics or a related field
- Experience
- 5+ years building deep-learning systems with a strong publication record
- Languages and tools
How to prepare
- Build a deep-learning forecaster on a noisy financial time series and show it beats simple baselines out of sample
- Study overfitting and evaluation in low signal-to-noise data
- Read recent papers on transformers for time series and reproduce one
- Practise PyTorch training and experiment tracking
What to study
- Financial Machine Learning
- Text as Data and NLP for Finance
- Econometrics and Financial Time Series
- Python for Quantitative Finance
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
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.