Senior Data Scientist, Finance - ML & Data Quality

Rachel Paul Recruiting

Greenwich (CT)

Hybrid

USD 150.000 - 230.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

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.

Qualifikationen

  • 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.

Aufgaben

  • 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.

Kenntnisse

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

Jobbeschreibung

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.

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