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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.

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.

• 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

• 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

• 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

• Rigorous, thorough, and highly attentive to detail
• Collaborative and able to communicate effectively with researchers and engineering partners
• Comfortable working in an evolving role and taking on a broad range of responsibilities

The salary range for this role is expected to be $180,000 to $200,000. This is the range that we in good faith believe is accurate for this role at the time of this posting. We may ultimately pay more or less than the posted range, depending upon factors such as skills, experience, location, or other business and organizational needs. This wage range may also be modified in the future.

This job is also eligible for an annual discretionary bonus.

We offer comprehensive package of benefits including paid time off, medical/dental/vision insurance, 401(k), and any other benefits to eligible employees.

Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company’s sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.