data scientist in financial crime

HireHi; Berlin; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Stock options

L&D budget €2,000

Pension matching 20%

28 days leave

Urban Sports Club

Kita placement

Office lunches subsidised

1-month sabbatical

Referral bonus

Zusammenfassung

SumUp is seeking an experienced ML/AI engineer in Berlin to build and operate end-to-end batch training pipelines for transaction-monitoring models. You will develop reliable software for testing, CI/CD, versioning, deployment, monitoring, and rollback across the model lifecycle, and collaborate with risk teams to tune features and thresholds.

Requirements include strong production Python engineering experience, reproducible ML pipelines, and clear communication.

Qualifikationen

• Strong production Python engineering experience with automated testing, CI/CD, and observability.
• Experience deploying and operating ML models in production with reproducible training and monitoring.
• Clear and confident communication to align stakeholders and translate requirements into technical plans.

Aufgaben

• Build and operate end-to-end batch training pipelines for transaction-monitoring models.
• Develop reliable software for testing, CI/CD, versioning, deployment, monitoring, and rollback.
• Improve maintainability and scalability of ML pipelines and model delivery.

Kenntnisse

Python

CI/CD

Communication

Tools

PySpark

Jobbeschreibung

Описание:

SumUp provides simple and affordable financial tools that help small businesses manage payments, finance, and customer relationships. More than 4 million businesses across 37 markets rely on SumUp as a financial partner.

Задачи:

Build and operate end-to-end batch training pipelines for transaction-monitoring models; Build reliable software for testing, CI/CD, versioning, deployment, monitoring, and rollback across the model lifecycle; Improve the maintainability, observability, and scalability of model pipelines; Partner with platform and software engineers to make model delivery repeatable and safe; Build, maintain, and improve ML models for transaction monitoring; Engineer features reflecting AML and Fraud typologies and suspicious behaviours; Work with Risk investigators to translate domain knowledge into signals, alerting logic, and calibrated thresholds; Analyse AML Risk Score drivers and recommend improvements to features, logic, and thresholds; Define and track model and operational metrics, including detection performance, alert volumes, and investigator outcomes; Monitor drift and model health, run back-testing, and investigate performance changes; Run sensitivity tests on synthetic datasets and assess model behaviour across relevant scenarios and populations; Produce model cards, technical documentation, and ML governance artefacts supporting auditability and regulatory review; Contribute to system-design documentation and adapt solutions to regional compliance requirements; Partner with AML and Fraud Operations, Product, and Engineering to turn ambiguous problems into clear, scalable technical plans; Explain trade-offs to technical and non-technical stakeholders; Help improve engineering practices, modelling approaches, and understanding of financial-crime risk; Share knowledge and support thoughtful experimentation, constructive challenge, and continuous improvement.

Требования:

Strong production Python engineering experience, including automated testing, CI/CD, code review, versioning, observability, and operating production services or pipelines; Experience deploying and operating ML models in production, including reproducible training, model versioning, deployment, monitoring, incident response, and rollback; Hands-on experience with end-to-end ML pipelines from data preparation and training through validation and production use; Understanding of appropriate KPIs and evaluation metrics; Solid data-engineering fundamentals with complex, multi-source data ecosystems; Focus on data quality, lineage, reproducibility, and failure modes; Willingness to deepen data-science expertise in modelling, feature engineering, evaluation, and experimentation; Clear and confident communication, with the ability to align stakeholders, set expectations, surface risks, and turn ambiguous compliance or operational requirements into concrete technical plans; Nice to have: Experience with PySpark or other distributed data-processing technologies, AML, fraud detection, transaction monitoring, or another financial-crime domain, unsupervised learning such as anomaly detection or clustering, feature stores, model registries, alerting-threshold calibration, ML governance artefacts such as model cards, validation reports, or audit documentation, AI systems and tooling.

Условия:

• Office-first setup from the Berlin office
• Virtual Stock Option programme
• Annual L&D budget of €2,000
• Corporate pension scheme matching up to 20% of contributions
• 28 Days of paid leave plus public holidays and special leave days
• Urban Sports Club subsidy, Kita placement assistance, and subsidised office lunches
• 1-Month sabbatical after 3 years of service
• Referral bonus.