Senior Data Scientist

Rachel Paul Recruiting; Greenwich (CT); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Quant Investment Firm in Greenwich, CT is seeking a Senior Data Scientist to bridge data engineering and quantitative research. You will ensure research datasets are accurate, traceable, and production-ready by building robust data prep and quality workflows.

You will deliver clean, reliable datasets and features to researchers, collaborate with researchers on ML projects, and help scale data pipelines across multiple initiatives.

Qualifications

• 4+ years of relevant work experience.
• Strong Python with pandas and NumPy.
• Strong SQL skills; PostgreSQL experience.
• Experience with data quality, validation, monitoring.
• Experience with Git, PyTest, CI/CD, and API development.
• Experience with scikit-learn, statistics, or advanced modeling.

Responsibilities

• Collaborate with quantitative researchers to produce data for ML projects.
• Transform raw data into research-ready datasets.
• Generate features and deliver datasets for modeling.
• Resolve tagging, entity-matching, and data linkage issues.
• Build data quality, profiling, and monitoring workflows.
• Create point-in-time mappings and knowledge graphs for corporate events.
• Onboard alternative datasets and ensure data reliability.
• Communicate with researchers and engineering teams.

Skills

Python

Pandas

NumPy

SQL

PostgreSQL

Data quality

Git

PyTest

CI/CD

API development

Communication

Tools

Claude Code

Codex

AWS Bedrock

AWS S3

AWS Batch

LLM APIs

Job description

My client is a top Quant Investment firm. They are looking for a Senior Data Scientist to join their team :) This role is hybrid in Greenwich, CT.

This Role requires Finance experience!

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.

What You'll Do:

• 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

What You'll Bring:

• 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

Preferred Qualifications:

• 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