Quantexa Transaction Monitoring Scenario Engineer

E
emagine PolskaFinancial Services
Fully remote in UKContractSenior
Salary not disclosed
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Job Details

Languages
English (C1)
Required Skills
SQLData modeling

Requirements

  • Proven experience in transaction monitoring scenario design within Quantexa.
  • Expertise in model calibration and tuning (thresholds, weights, optimisation).
  • Experience with scenario modelling and rule design.
  • Knowledge of customer and transaction segmentation strategies.
  • Hands-on experience with Quantexa platform capabilities, including configuration, data ingestion, and network-based analytics.
  • Solid experience in testing methodologies (back-testing, sensitivity analysis, performance tuning).
  • Strong understanding of model validation frameworks, governance, and audit requirements.
  • Experience translating analytical requirements into technical specifications.
  • Knowledge of AML/KYC regulations (CDD, EDD, SAR/STR reporting).
  • Experience working within financial crime transformation or data-driven programmes.
  • Familiarity with data modelling, architecture, and integration in complex environments.

Responsibilities

  • Design, build, and optimise Quantexa transaction monitoring scenarios, leveraging entity resolution, network analytics, and contextual data.
  • Lead model calibration and tuning activities, including threshold optimisation, score weighting, and segmentation strategies.
  • Develop and maintain scorecards and risk models, incorporating behavioural indicators and customer segmentation.
  • Own the end-to-end model testing lifecycle, including back-testing, forward-testing, and sensitivity analysis.
  • Support model validation processes, providing documentation, assumptions, and performance metrics.
  • Translate business and regulatory requirements into Quantexa configurations, data models, and analytical logic.
  • Collaborate with engineering, data, and architecture teams to implement models within the Quantexa ecosystem.
  • Drive improvements in data quality, lineage, and governance.
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