Quant jobs / Schonfeld

AI Data Scientist

A hands-on data scientist and engineer building pipelines and datasets for Schonfeld's internal AI platform used in investment research. The role mixes data engineering, analysis and AI infrastructure.

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.

Build batch and real-time pipelines for structured and unstructured data
Engineer features and research-ready datasets for model training and evaluation
Maintain vector databases and semantic search for LLM applications
Track data versioning, lineage and quality for AI workflows

What they ask for

Required

Python and SQL
Pipeline orchestration (Airflow, Prefect, Dagster)
Schema design with SQL and NoSQL stores
ML tooling (XGBoost, PyTorch, TensorFlow)
Cloud platform (AWS, GCP or Azure)
Vector databases and embeddings

Preferred

Buy-side or sell-side data experience
LLM and RAG support
Spark or Flink
Financial data sources
Kafka-style streaming
Data quality frameworks

Education: Bachelor's, Master's or PhD in computer science, data science or a related field.
Experience: Experience building production data pipelines; years not stated
Languages and tools: Python
SQL
Java
Scala
Go

How to prepare

Build an orchestrated ETL pipeline with data quality checks
Create an embedding and vector-search system over financial documents
Learn feature engineering and model evaluation with XGBoost
Study the shape of market, fundamental and alternative datasets

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Schonfeld’s site before applying: requirements, location and pay can change after we check them.

View the posting on Schonfeld’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Schonfeld and doesn’t handle applications.