Data Scientist
An entry-level data scientist role for 2026 or 2027 graduates, using machine learning to build predictive datasets from financial and unstructured data. It sits in WorldQuant's research offices with mentoring.
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.
- Model financial datasets with machine learning
- Validate unstructured data and turn it into signals
- Build datasets intended to predict market movements
- Learn markets through one-to-one mentoring
What they ask for
Required
- Bachelor's, Master's or PhD in progress in a quantitative field
- Graduating in 2026 or 2027
- At least one programming language
- Linux/Unix familiarity
- English reading and writing
Preferred
- NLP, deep learning or AI research experience
- Education
- Currently pursuing a bachelor's, master's or PhD in a related field
- Experience
- New graduates; no finance experience required
- Languages and tools
How to prepare
- Build a model on a public financial dataset and assess it out of sample
- Practise feature engineering on text or other unstructured data
- Learn core NLP and deep learning techniques
- Read an introduction to financial markets
What to study
- Financial Machine Learning
- Text as Data and NLP for Finance
- Python and Data Primer
- Probability, Statistics and Financial Data
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 WorldQuant’s site before applying: requirements, location and pay can change after we check them.
View the posting on WorldQuant’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with WorldQuant and doesn’t handle applications.