Senior Data Scientist (d/f/m)

Adevinta 2021; Berlin; Vor Ort; EUR 80.000 - 100.000; Vollzeit

Zusammenfassung

An innovative technology company in Berlin is seeking a Senior Data Scientist to enhance fraud detection using ML models. The ideal candidate has over 5 years of experience in ML and deep learning, with strong Python skills and a Master's degree in a related field. This role involves agile teamwork and continuous learning to ensure data quality and model integrity in production.

Qualifikationen

• 5+ years of experience applying ML and deep learning methods in production.
• Strong proficiency in Python and ML libraries.
• Familiarity with cloud-based environments for deployment.

Aufgaben

• Designing and enhancing production-grade ML models for fraud detection.
• Monitoring and evaluating ML models in production.
• Collaborating with cross-functional teams to apply ML effectively.

Kenntnisse

Machine Learning

Deep Learning

Python

Feature Engineering

Data Quality Assurance

Ausbildung

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

Tools

scikit-learn

PyTorch

AWS

SageMaker

Jobbeschreibung

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.

Responsibilities

• Understand fraud patterns, user trust needs and identify where Machine Learning can bring the greatest impact.
• Develop ML models from scratch or fine-tune existing ones for fraud detection and behavioural analytics
• Train and test deep learning models.
• 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.

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 and deep learning methods to build and deploy production-grade models (e.g., XGBoost, Random Forests, Logistic Regression, Neural Networks, Transformers) ideally in fraud detection or Trust & Safety domain, ensuring quality and robustness of data science outputs.
• 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.
• Practical experience with Generative AI and Large Language Models (LLMs) for tasks such as classification, summarization, or risk signal extraction from unstructured text, with a clear understanding of evaluation, prompt design, and ethical considerations in production use.
• 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.
• True team player mentality, with excellent communication skills including ability to explain complex ML results to non-technical stakeholders.
• Proactive collaboration with data and application engineers to shape data models and ensure ML-readiness for production.
• Awareness of ethical and regulatory concerns in AI systems.