Data Scientist - AI for Financial Crime & Pilot Deployments

Thetaray; Madrid; Presencial; EUR 65.000 - 90.000; Jornada completa

Descripción de la vacante

ThetaRay is seeking a Data Scientist to translate advanced data science and ML capabilities into real business value for customers in financial crime detection. You will lead end-to-end deployments that sit at the intersection of AI and finance, guiding projects from design through pilot phases.

You will design customer-specific technical solutions, lead data science and engineering efforts, engineer features from diverse data sources, uncover insights, train users, and ensure robust monitoring.

Formación

• 2+ years of experience as a data scientist/data engineer/big‑data developer.
• BSc or equivalent in Mathematics, Statistics, Computer Science, Economics, or related field.
• Fluent English - written and spoken.
• Python/PySpark and DS workflow tools including MLflow, Airflow.
• Experience in data analytics for financial services and data mining.

Responsabilidades

• Deliver successful deployment and pilots.
• Manage and design the Customer-specific technical solution throughout the project life cycle.
• Technical leadership of Data Science and engineering, including partners.
• Work on various data sources and apply feature engineering capabilities.
• Extract insights and actionable recommendations from large data and investigate anomalies.
• Train customers on system usage and monitoring aspects.

Conocimientos

Python

PySpark

MLflow

Airflow

Data analysis

Big Data

Statistics

Linux

English

Educación

BSc or equivalent

Herramientas

Linux OS

Descripción del empleo

ThetaRay is seeking a Data Scientist to translate advanced data science and ML capabilities into real business value for customers in financial crime detection. You will lead end-to-end deployments that sit at the intersection of AI and finance, guiding projects from design through pilot phases.

You will design customer-specific technical solutions, lead data science and engineering efforts, engineer features from diverse data sources, uncover insights, train users, and ensure robust monitoring.