Data Scientist, Machine Learning

AQR•Greenwich, CT

1d•$180,000 - $200,000

About The Position

AQR is a global investment firm built at the intersection of financial theory and practical application. We strive to deliver concrete, long-term results by looking past market noise to identify and isolate the factors that matter most, and by developing ideas that stand up to rigorous testing. By putting theory into practice, we have become a leader in alternative strategies and an innovator in traditional portfolio management since 1998. At AQR, our employees share a common spirit of academic excellence, intellectual honesty and an unwavering commitment to seeking the truth. We’re determined to know what makes financial markets tick – and we’ll ask every question and challenge every assumption. We recognize and respect the power of collaboration and believe transparency and openness to new ideas leads to innovation. AQR is a global investment management firm built at the intersection of financial theory and practical application. We strive to deliver superior, long-term results for our clients by seeking to filter out market noise to identify and isolate what matters most, and by developing ideas that stand up to rigorous testing. Underpinning this philosophy is an unrelenting commitment to excellence in technology - powering our insights and analysis. This unique combination has made us leaders in alternative and traditional strategies since 1998. AQR takes a systematic, research-driven approach, applying quantitative tools to process fundamental information and manage risk. Our clients include institutional investors, such as pension funds, insurance companies, endowments, foundations and sovereign wealth funds, as well as financial advisors. Your Role: You will serve as a bridge between data engineering and quantitative research. Working directly with researchers, you will also be responsible for ensuring research datasets are accurate, traceable, and ready for machine learning by developing robust data preparation and quality workflows. Your responsibility is to deliver clean, reliable, project-specific datasets and features to the researcher.

Requirements

4+ years of relevant work experience
Strong Python programming skills, including hands-on experience with pandas and NumPy
Strong SQL skills and practical experience with PostgreSQL
Experience working with both structured data and unstructured or textual data
Experience with data quality, validation, monitoring, and profiling
Experience with Git, PyTest, CI/CD, and API development
Ability to reason carefully through edge cases, protect data integrity, and maintain clear documentation of data definitions, transformations, and quality checks
Ability to work independently, communicate clearly with technical and non-technical stakeholders, and manage work across multiple concurrent initiatives
Strong visualization skills

Nice To Haves

Experience with scikit-learn, statistics, or advanced modeling techniques
Experience with entity resolution, entity matching, or knowledge graphs
Experience designing prompts and using LLM APIs for batched or large-scale investigation, validation, feature generation, and iterative refinement
Experience building LLM-based featurization workflows, including iterative refinement, validation, and automated testing
Experience with Claude Code, Codex, or AWS Bedrock
Experience with AWS, including S3 and Batch
Experience with distributed computing and large-scale data processing
Exposure to data governance, data cataloging, or related best practices
Strong Math and statistics skills
Experience with Pytorch
Prior experience in financial services, trading, quantitative research, or another research-driven environment

Responsibilities

Partner directly with quantitative researchers to understand the needs of a specific machine learning project and collaboratively produce data that best fits the model and project
Transform raw structured and unstructured data into project-specific, research-ready datasets
Perform feature generation and deliver prepared datasets and features to the researcher for modeling and productionization
Resolve tagging, entity-matching, and linkage issues across signals, textual data, and securities
Build data quality assurance, quality monitoring, and profiling workflows through programmatic checks, LLM reviews where appropriate, targeted manual inspection, and feedback-driven iterative refinement
Build point-in-time mappings and knowledge graphs for mergers and acquisitions, bankruptcies, IPOs, and other corporate events
Examine and onboard alternative datasets
Ensure datasets are clean, traceable, and reliable for trading strategies
Work across different researchers and potentially concurrent projects as priorities and the scope of the role evolve
Communicate clearly with researchers and engineering partners