Data Scientist – Fraud

Overview

Veriff pursues the mission of creating a safer, more secure world by establishing trust online. The Fraud Engineering team contributes directly to this mission by developing the systems and models that identify and stop fraudsters before they succeed.

We are building the next-generation fraud detection platform, designed to evolve, scale, and stay ahead of sophisticated fraud threats. This position offers the opportunity to contribute to the foundation of that system.

We seek a Data Scientist who thrives on complex challenges, understands real-world fraud dynamics, and delivers measurable impact. The successful candidate will collaborate with fraud analysts, engineers, and product managers to develop models, uncover fraud patterns, and integrate their work into production environments at scale.

Responsibilities

Help design and evolve fraud detection mechanisms underpinning Veriff’s next-generation fraud detection platform.
Build and refine real-time models to identify and block fraud before it impacts customers.
Invent new detection strategies that outpace fraudsters and adapt to shifting attack vectors.
Design and validate data-driven hypotheses to identify emerging fraud patterns, and transform them into scalable detection features and model improvements.
Collaborate with engineers to deploy and scale models within production systems.
Define model monitoring strategies, assess business impact, and iterate based on results.
Investigate the latest advances in fraud analytics and applied machine learning.

Company:

Veriff

Qualifications:

Minimum of 7 to 8 years’ experience as a Data Scientist.
Familiarity with production deployment of machine learning models.
Proficiency in Python and SQL.
Strong capabilities in data manipulation and visualisation using tools such as Pandas/Polars, NumPy, and Matplotlib/Seaborn.
Practical experience with machine learning frameworks such as scikit-learn, XGBoost/LightGBM/CatBoost.
Solid understanding of both supervised and unsupervised learning techniques.
Ability to communicate findings clearly with technical and non-technical stakeholders.

Desirable

Experience within fraud detection, financial crime, trust and safety, or related domains.
Experience working with embeddings and vector similarity search.
Experience working in cloud environments, such as AWS, GCP, or Azure.

Level of experience (years):

Senior (5+ years of experience)

Tagged as: Data Mining, Industry, Machine Learning, Spain, Supervised Learning, Unsupervised Learning