Data Science Intern: AI-Driven Outlier Detection & News

Capital Fund Management (CFM); Paris; Hybride; EUR 7 800 - 12 000; Plein temps

Résumé du poste

Capital Fund Management in Paris is offering an internship in Automated Data Quality & News-Driven Outlier Detection. You will join the Equity Data Referential team to ensure equity timeseries are accurate, clean, and trustworthy.

You will develop automated methods to reduce false positives by leveraging AI tools and historical News datasets, and you will benchmark results against past validations to improve data quality pipelines for non-production stocks.

Qualifications

• Pursuing data science studies at Master’s level or equivalent.
• Aptitude for data analysis, problem investigation, and automation of manual processes.
• Comfort with large, high-dimensional datasets and leveraging AI tools.
• Fluent in French and English.

Responsabilités

• Develop automated approaches for each detected outlier to reduce false positives.
• Correlate outliers with News data, including sentiment and corporate events.
• Benchmark data quality results against historical price validations.
• Handle large News datasets with efficient data processing and feature selection.
• Contribute to improvement of the data quality pipeline for non-production stocks.
• Explore AI-driven integration into the existing outlier detection workflow.
• Build reusable, scalable tooling to support production-readiness.

Connaissances

Data analysis

Automation

Large datasets

Bilingual FR/EN

Formation

Master's degree in data science or related field

Outils

SQL

Python

NLP

Description du poste

Capital Fund Management in Paris is offering an internship in Automated Data Quality & News-Driven Outlier Detection. You will join the Equity Data Referential team to ensure equity timeseries are accurate, clean, and trustworthy.

You will develop automated methods to reduce false positives by leveraging AI tools and historical News datasets, and you will benchmark results against past validations to improve data quality pipelines for non-production stocks.