Data Scientist for Transaction Monitoring - Analytics & Data Science

Credit Suisse

Lausanne, Switzerland

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We Offer

  • The opportunity to become part of our highly motivated Analytics team at Credit Suisse Lausanne focusing on building and driving new and state of the art technologies in various fields
  • A meaningful role as Data Scientist in a demanding, dynamic and international environment using the latest innovations in predictive analytics and visualization techniques
  • You will get the chance to engage with external companies in automating regulation and compliance to build a state-of-the art regulatory reporting and analytics infrastructure
  • You will have the responsibility for developing, maintaining and extracting knowledge from strategic internal and external data sets
  • Studying fundamental and high impact business questions that directly affect the direction of the company and the industry at large
  • Developing and designing algorithms, building prototype versions, running multiple validations with business guides and working on their operationalization
  • Becoming part of an open-minded team with a strong team spirit in a versatile, dynamic, and flexible working environment
  • Flexible / agile working options are possible

You Offer

  • A PhD or Master degree in Computer Science or in a quantitative field (Statistics, Mathematics, Economics), in Data Science or an equivalent education
  • Proficiency in at least one of the following programming languages Python, Java, Scala, R and/or SQL.
  • Experience with relational database programming and distributed data processing at scale using Spark or/and Hive
  • Confirmed Experience in Statistical Modelling (Classification, Scorecard Models, Segmentation, Clustering, Bayesian statistics), Anomaly Detection, and / or Machine Learning algorithms both Supervised and Unsupervised
  • Good experience in the regulatory technologies or compliance domain within the financial industry
  • Familiarity with crafting and analyzing data sets from disparate sources applying quantitative methodologies, computational frameworks and systems


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