ML Engineer

Enfint; Berlin; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Office in Berlin

Stock options

L&D budget €2000

Pension scheme match 20%

28 days paid leave

Urban Sports Club subsidy

Kita placement assistance

Referral bonus

Zusammenfassung

SumUp ищет специалиста по машинному обучению для построения и развёртывания production ML систем, включая пайплайны обучения, версионирование моделей и мониторинг для трансакционных моделей мониторинга.

Задача — развивать модели AML и Fraud, работать над признаками и порогами, взаимодействовать с командами риска и регуляторными требованиями, обеспечивая качество детекции и соответствие нормам.

Qualifikationen

• Write production Python code with CI/CD, automated testing, versioning, and monitoring experience.
• Experience deploying and operating ML models in production, including drift monitoring and rollback.
• Train and productionize ML models and choose appropriate evaluation KPIs and metrics.
• Hands-on experience with complex, multi-source data ecosystems, data quality, and data lineage.
• Communicate clearly with cross-functional stakeholders, translate regulatory requirements into concrete technical plans.
• Nice to have: PySpark, AML, fraud detection or financial crime experience, unsupervised machine learning, Feature Stores, alerting threshold calibration, regulatory ML governance artefacts, AI systems and tooling.

Aufgaben

• Build and ship production ML systems end to end, including batch training pipelines, model versioning, monitoring, deployment, and rollback for transaction monitoring models.
• Build, maintain, and improve ML models for transaction monitoring, focusing on detection quality, operational efficiency, and regulatory compliance.
• Engineer features mapped to AML and Fraud typologies and suspicious behaviours, working with Risk investigators to translate domain knowledge into alerting logic and threshold calibration.
• Run sensitivity tests on synthetic datasets and produce ML governance artefacts such as model cards and audit-ready documentation.
• Own and evolve the AML Risk Score by analysing driver contributions, monitoring drift, running back-testing, and recommending improvements to features, logic, and thresholds.
• Partner with AML and Fraud Operations, Product, and Engineering to translate stakeholder needs into actionable, scalable data science solutions.
• Track and improve detection performance metrics, adapt solutions to regional compliance requirements, and contribute to system design documentation.

Kenntnisse

Python

CI/CD

Production ML deployment

Data quality & lineage

Stakeholder communication

ML governance

Tools

PySpark

AML

Fraud detection tools

Feature Stores

Jobbeschreibung

Описание

SumUp provides simple and affordable financial tools that help small businesses manage payments, finance, and customer relationships. More than 4 million businesses in 37 markets rely on its products.

Задачи

• Build and ship production ML systems end to end, including batch training pipelines, model versioning, monitoring, deployment, and rollback for transaction monitoring models
• Build, maintain, and improve ML models for transaction monitoring, focusing on detection quality, operational efficiency, and regulatory compliance
• Engineer features mapped to AML and Fraud typologies and suspicious behaviours, working with Risk investigators to translate domain knowledge into alerting logic and threshold calibration
• Run sensitivity tests on synthetic datasets and produce ML governance artefacts such as model cards and audit-ready documentation
• Own and evolve the AML Risk Score by analysing driver contributions, monitoring drift, running back-testing, and recommending improvements to features, logic, and thresholds
• Partner with AML and Fraud Operations, Product, and Engineering to translate stakeholder needs into actionable, scalable data science solutions
• Track and improve detection performance metrics, adapt solutions to regional compliance requirements, and contribute to system design documentation

Требования

• Write production Python code with CI/CD, automated testing, versioning, and monitoring experience
• Have experience deploying and operating ML models in production, including drift monitoring and rollback
• Train and productionize ML models and choose appropriate evaluation KPIs and metrics
• Have hands-on experience with complex, multi-source data ecosystems, data quality, and data lineage
• Communicate clearly and confidently with cross-functional stakeholders, set expectations, and turn ambiguous compliance requirements into concrete technical plans
• Nice to have: Pyspark, AML, fraud detection or financial crime experience, unsupervised machine learning, Feature Stores, alerting threshold calibration, regulatory ML governance artefacts, AI systems and tooling

Условия

• Office-first setup in Berlin
• 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
• One-month sabbatical after 3 years of service
• Referral bonus