ML Data Scientist for Trading Research & Data Quality

Tradermath; Greenwich (CT); Hybrid; USD 180,000 - 200,000; Full time

Benefits offered by this job

Annual discretionary bonus

Benefits package

401(k)

Job summary

AQR Capital Management is seeking a data engineer bridging data engineering and quantitative research. You will ensure research datasets are accurate, traceable, and ready for machine learning by building robust data pipelines and quality workflows.

Collaboration with researchers and engineers is essential as you transform raw data into project-specific datasets, generate features, and support modeling and productionization. A strong focus on data integrity and scalability drives your daily work.

Qualifications

• 4+ years of relevant work experience.
• Strong Python programming skills with pandas and NumPy.
• Strong SQL skills and PostgreSQL experience.
• Experience with both structured and unstructured data.
• Experience with data quality, validation, monitoring, and profiling.
• Experience with Git, PyTest, CI/CD, and API development.
• Ability to document data definitions, transformations, and quality checks.
• Ability to work independently and communicate with stakeholders.
• Strong visualization skills.

Responsibilities

• Partner with quantitative researchers to understand data needs for ML projects.
• Transform raw data into research-ready datasets.
• Generate features and deliver datasets for modeling.
• Resolve tagging and entity-matching across signals and texts.
• Build data quality, profiling, and monitoring workflows.
• Develop mappings and knowledge graphs for corporate events.
• Onboard additional datasets and ensure data reliability for trading.
• Communicate clearly with researchers and engineering partners.

Skills

Python

SQL

Git

API development

Pandas

NumPy

Data quality

Documentation

CI/CD

LLM prompts

Pytorch

Visualization

Tools

PostgreSQL

AWS

S3

Distributed computing

PyTest

Job description

AQR Capital Management is seeking a data engineer bridging data engineering and quantitative research. You will ensure research datasets are accurate, traceable, and ready for machine learning by building robust data pipelines and quality workflows.

Collaboration with researchers and engineers is essential as you transform raw data into project-specific datasets, generate features, and support modeling and productionization. A strong focus on data integrity and scalability drives your daily work.