What You'll Do & Who You Are
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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.
Your Role
* Understand fraud patterns, user trust needs and identify where Machine Learning can bring the greatest impact.
* Train and evaluate 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, compare variants and identify 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.
* Proactively collaborate 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.
Benefits
* An attractive Base Salary
* Participation in our Short‐Term Incentive plan (annual bonus)
* Work From Anywhere: Enjoy up to 20 days a year of working from anywhere! Maybe not from the moon, well why not! Just make sure you have internet connection.
* A 24/7 Employee Assistance Program for you and your family, because we care.
* Win together, lose together is one of our key behaviours. At Adevinta you will find a collaborative environment with an opportunity to explore your potential and grow.
Adevinta is an equal opportunity employer and we value diversity. xohynlm We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status.