Analytics Senior Analyst (f/m/x) AFC Screening Model Data & Model Engineer

at  Deutsche Bank

YOUR SKILLS AND EXPERIENCES

• Strong Analytics skills including data modeling, data engineering, advanced SQL knowledge, Python or Go and use of analytics platforms such as Cloudera Data Science Workbench (CDSW) or similar. GitHub, Kaggle or similar profiles welcome. Familiar with Linux and Unix shell scripting. Tableau for visualization also welcome.
• Experience in machine learning operations (ML Ops), model engineering and creating UX design for MI and reporting purpose -> Experience with Financial Crime related engines / platforms would be preferable along with model / data engineering and exposure in machine learning operations (ML Ops)
• Demonstrated ability to productionize, deploy and operate models, preferable in a regulated function
• Experience with Fircosoft, RDC, HotScan or related name/transaction screening and negative news screening systems
• Mathematics, statistics or engineering background preferred
• Previous exposure speaking to global regulators and explaining model design and failures

DETAILS OF THE ROLE AND HOW IT FITS INTO THE TEAM

Deutsche Bank (DB) benefits from having a highly experienced and dedicated Anti Financial Crime (AFC) function, which performs a crucial role in keeping DBs business operations and global financial services clean from financial crime while serving the interests of the bank and society.
In order to combat financial crime effectively and respond to challenges in a flexible manner, AFC has a matrix structure combining regional, business line and global functional coverage in our core areas of Anti-Money Laundering , Sanctions & Embargoes , Anti-Fraud, Bribery & Corruption , Investigations & Intelligence , Monitoring & Screening, and Risk Assessment . Part of our commitment is to strengthen a strong collaboration with all business lines and support functions within our bank.

YOUR KEY RESPONSIBILITIES

• Assisting with the assessment or risks related to name and transaction screening models, and the definition of the testing standards. Creating auditable and traceable documentation of data pipelines, queries and visualizations. Implementation of data load schedules and data transformations
• Supporting validations and changes to existing screening models and collaborating within the function to ensure new data elements used in models, changed data requirements and features are appropriately controlled to ensure nominal model operation
• Develop deep understanding of fundamental model data structures to assess suitability of testing procedures and develop appropriate testing methodologies assessing the risk. Liaising suggestions on senior stakeholders and acting on feedback in a timely and efficient manner.
• Support the development of appropriate methodologies to tune and optimize models in an efficient manner, and use SQL and Spark and perform data investigation and analysis identifying gaps or issues related to existing procedures
• Investigation of new and emerging evasion tactics for sanctions and AML detection mechanisms and implementation of emerging typologies
• Develop documentation capturing all appropriate model assessment metrics, quantification of risks to inform threshold decisions and metric changes, investigate these and action on any mitigation required. Build MI/Reporting data schemas or ad-hoc report for regulators and senior internal stakeholders