Vienna-Based AML Data Scientist — Production ML & Pipelines

Raiffeisen Bank International AG

Warszawa

Presencial

PLN 262.000 - 394.000

Tempo integral

14 dias+

Uma candidatura completa em um minuto

Currículo e carta de apresentação personalizados, prontos para serem enviados.

Passe pelos filtros ATS

Benefícios oferecidos por esta vaga

Mindgram access
Group Insurance
Employee Capital Plans

Resumo da vaga

Raiffeisen Tech GmbH in Vienna seeks a Data Scientist to bridge Data Science, Data Engineering, and Financial Crime Prevention, delivering ML solutions across banking environments.

You'll develop models, work with large datasets, and collaborate with AML experts and engineering teams in an international setting. The role emphasizes production-ready ML, data quality, and continuous improvement.

Qualificações

  • Experience as a Data Scientist, ML Engineer, or similar analytical role.
  • Hands-on ML model development and lifecycle management.
  • Strong Python and SQL skills with large datasets.
  • Familiarity with Databricks or Big Data platforms is a plus.

Responsabilidades

  • Develop and enhance ML models supporting AML processes.
  • Analyze large-scale datasets and improve model performance.
  • Support deployment, monitoring, and maintenance of ML in production.
  • Investigate data quality issues across data sources.
  • Collaborate with Data Engineers on pipelines, features, and prep.
  • Contribute to continuous improvement of analytical solutions.
  • Work with AML experts, stakeholders, and tech teams in an international setting.

Conhecimentos

Machine Learning
Python
SQL
Data analysis
Data engineering concepts
Databricks
Data quality
Communication in international teams

Descrição da vaga

Raiffeisen Tech GmbH in Vienna seeks a Data Scientist to bridge Data Science, Data Engineering, and Financial Crime Prevention, delivering ML solutions across banking environments.

You'll develop models, work with large datasets, and collaborate with AML experts and engineering teams in an international setting. The role emphasizes production-ready ML, data quality, and continuous improvement.

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