Risk Model Management Specialist – Financial Crime Prevention (AML)

100 PLN/ godz./ godz.B2BB2B (netto)

MidHybryda·WarszawaFull-time·B2B

#462807·Dodano 4 dni temu·0

Źródło: justjoin.it

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Tech Stack / Keywords

Data ScienceModel validationModel GovernanceRisk Management

Podsumowanie oferty

Own the development, validation and governance of transaction monitoring models for a major Nordic bank, as part of its Financial Crime Prevention team. The role combines model risk expertise with hands-on analytical work.

Wymagania

Extensive experience in model risk management, model governance and model validation.
Strong background in data science, statistics and quantitative methods.
Experience in academic or technical writing, especially model documentation.
Advanced degree in a quantitative field such as Computer Science, Statistics, Mathematics or Actuarial Science.
Strong analytical and problem-solving skills.
Excellent communication, presentation and stakeholder management skills.
Structured, proactive approach, with the ability to take initiative and work independently.
Confidence setting direction and delivering results in a fast-changing environment.
Fluent spoken and written English.

Nice to have:

Experience in Financial Crime or AML, especially transaction monitoring.
Hands-on work with transaction monitoring platforms such as Quantexa.
Experience as a model validator or quantitative risk analyst.
Banking background.

Obowiązki

Own the development and validation of transaction monitoring models, focusing on documentation, review and governance.
Lead model governance activities within TMU, including validation assessments and implementing feedback from the second line of defence.
Define and carry out the approach to model development and experimental design.
Document model design and outcomes with the analytics team, accurately capturing assumptions, limitations and weaknesses.
Support prototyping of tests and analytical methods hands-on.
Mentor the analytics team and work with them to design, test and implement model SOPs, including data lineage and governance, tuning and calibration, and monitoring model operations and outcomes.
Work professionally with internal and external stakeholders.

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