Senior Data Scientist

Adevinta 2021; Barcelona; Presencial; EUR 70.000 - 90.000; Jornada completa

Descripción de la vacante

A leading technology platform in Barcelona seeks a Senior Data Scientist to enhance machine learning models that detect fraud and assess user risk. The successful candidate will have at least 5 years of experience, strong proficiency in Python and ML libraries, and a Master’s degree in a relevant field. You will work with agile teams to ensure data integrity and promote ethical AI use, making a significant impact on building trust in online platforms.

Formación

• 5+ years of experience applying ML methods to production-grade models.
• Strong proficiency in Python and ML libraries.
• Practical experience with Large Language Models.

Responsabilidades

• Design, build, and enhance machine learning models.
• Monitor and evaluate ML models in production.
• Collaborate with agile cross-functional teams.

Conocimientos

Machine Learning

Python

Data Science

Feature Engineering

Communication Skills

Educación

Master’s degree in computer science, data science, statistics, or mathematics

Herramientas

scikit-learn

PyTorch

AWS

Descripción del empleo

We are looking for a Senior Data Scientist to join our mission to make our platform an even safer place to trade. You will be responsible for designing, building, and continuously enhancing production-grade, end-to-end machine learning models that detect fraud and assess user risk in the Trust and Safety domain. You will be part of a cross-functional business area composed of multiple teams including experts from Product, Customer Service, Analytics, Data and Engineering. Together, you’ll tackle one of the most meaningful challenges in online platforms: building trust at scale.

This is your opportunity to improve the experience of millions of users and have an impact by building a platform that enables sustainable trade for everyone.

Your role:

• Understand fraud patterns, user trust needs and identify where Machine Learning can bring the greatest impact.
• Train and evaluation of ML models from scratch or fine-tune existing ones for fraud detection and behavioural analytics
• Work as part of an agile cross-functional development team with a “win together, lose together” mindset, having end-to-end responsibility from design and development to deployment, monitoring, and maintenance in production.
• Engineer and select features from large, complex datasets to improve model accuracy and robustness.
• Monitor and evaluate ML models in production, conduct model experiments, comparing variants and identifying improvement and retraining needs.
• Ensure data and model quality, integrity, and reproducibility in production environments.
• Share your knowledge, evolve best practices with your colleagues to boost machine learning at Kleinanzeigen strengthening our ML community.
• Proactively identify opportunities to apply ML for fraud detection and increased user trust.
• Promote ethical AI use, ensuring fairness, transparency, and accountability in all models developed.
• Proactive collaboration with data and application engineers to shape data models and ensure ML-readiness for production.

Qualifications

• Master’s degree in computer science, data science, statistics, mathematics or related field (or equivalent experience)
• At least 5+ years of proven experience applying ML methods to build and deploy production‑grade models (e.g., XGBoost, Random Forests, Logistic Regression, Neural Networks, Transformers)
• Strong proficiency in Python and ML libraries (e.g., scikit‑learn, PyTorch, XGBoost), with proven experience applying classical ML to structured and time series data, including feature engineering, model evaluation (e.g., precision/recall, AUC), and deploying scalable models (e.g., XGBoost, Random Forests, Logistic Regression) to production.
• Solid understanding of ML/DS best practices, including model validation, A/B testing, feature engineering, and pipeline management ensuring quality and robustness of data science outputs.
• Practical experience with Large Language Models (LLMs) for tasks such as classification, summarization, or risk signal extraction from unstructured text, with a clear understanding of evaluation and ethical considerations in production use.
• True team player mentality, with excellent communication skills including ability to explain complex ML results to non-technical stakeholders.

Preferred qualifications

• Knowledge and experience with fraud detection or Trust & Safety domain
• Familiarity with cloud-based environments (e.g., AWS) and production ML tools (e.g., SageMaker, Airflow, MLflow).
• Experience working in Agile teams with modern DevOps/dataops practices.
• Awareness of ethical and regulatory concerns in AI systems.