Gcoo - Afc Modelling Analytics Analyst - Vp

**GCOO - AFC Modelling Analytics Analyst - VP**:
**Job ID**:R0390088

**Full/Part-Time**:Full-time

**Regular/Temporary**:Regular

**Listed**:2025-07-09

**Location**:Singapore

**Position Overview**:
**Details of the Division and Team**:
The Group Strategic Analytics TM modelling teams remit covers the design and development of robust and comprehensive Transaction Monitoring models for the Anti Financial Crime function. The team also performs critical model calibration and optimisation tasks on a periodic basis. Additionally, the team covers critical business support initiatives requiring advanced data analytics, data science and knowledge of business data. These tasks involve the use of the latest cloud based technologies in conjunction with advanced programming techniques

**What we will offer you**:
A healthy, engaged and well-supported workforce is better equipped to do their best work and, more importantly, enjoy their lives inside and outside the workplace. That’s why we are committed to providing an environment with your development and wellbeing at its center.

**You can expect**:

- Flexible benefits plan including virtual doctor consultation services
- Comprehensive leave benefits
- Gender Neutral Parental Leave
- Flexible working arrangements
- 25 days of annual paid leave, plus public holiday & Flexible Working Arrangement

**Your key responsibilities**:

- Provide expert guidance in selection, set-up and tuning of AML detection rules for transaction monitoring, adapting to risk typologies relevant for the bank
- Perform advanced analytics on structured and semi-structured payment data, incorporating transaction metadata, behavior patterns, and alert feedback loops.
- Design and develop custom transaction monitoring models to integrate with the banks existing transaction monitoring ecosystem
- Integrate payment ecosystem knowledge (e.g., card payments, real-time payments, cross-border transfers) into data models for comprehensive monitoring coverage.
- Map financial crime typologies (e.g., layering, mule networks, terrorist financing, shell entity detection) into detection frameworks that align with FATF, MAS, RBI, FinCEN, BaFin, and EU AMLD requirements.
- Work with messaging standards (e.g., SWIFT MT/MX, ISO 20022) to extract risk-relevant fields and design rules accordingly.
- Contribute to Entity Resolution strategies and the consolidation of risk signals across counterparties, wallets, and accounts.
- Design analytics to monitor activity related to virtual asset service providers and peer-to-peer transfers with risk indicators for anonymity-enhanced transactions.
- Support model governance through rigorous documentation, validation, audit preparedness, and alignment with model review functions

**Your skills and experience**:

- 10+ years in data analytics with at least 7 years in AML, fraud detection, payments, or financial crime analytics.
- Minimum 10 years of expertise in SQL and Python, including work with complex queries across large transactional datasets.
- Minimum 10 years of hands-on experience with AML systems (Actimize, Mantas, SAS AML, Oracle FCCM, etc.).
- Bachelor’s or Master’s Degree qualification in Data Science, Computer Science, Finance, or related field.
- Experience of working in cloud environments, and big data platforms and related technologies e.g., AWS, GCP, Snowflake, HDFS, Apache Spark, Big Query.
- In-depth understanding of AML frameworks (e.g., BSA/AML, FATF 40 Recommendations) and compliance obligations.
- Familiarity with MAS Notices 626/824, FinCEN SAR rules, BaFin’s GwG, and EU AMLD (5 & 6) requirements.
- Exposure to payment systems and standards, such as:

- ISO 20022, SWIFT MT/MX
- Card schemes (Visa/MC), ACH, SEPA, RTP (Real-Time Payments), CHAPS
- Cross-border settlement mechanisms and PSPs
- Knowledge of virtual assets, VASPs, crypto exchanges, and relevant FATF guidance on digital asset risk.
- Experience with FinTech business models, including e-wallets, P2P apps, challenger banks, and API-based ecosystems.
- Advanced programming knowledge in Python and R with exposure to advanced data structures, OOP, database frameworks, statistical modeling, prototyping, and rule testing.
- Familiarity with Entity Resolution techniques and graph/network analysis.
- Experience with visualization/reporting tools like Tableau, Power BI, or Looker.
- Understanding of anomaly detection, clustering, or other ML techniques in the AML domain.
- Prior engagement with Regulators, FIUs, or Internal Audit on AML model assurance.

Role is required to be performed on-site at One Raffles Quay office. Relevant vaccination requirements may apply.

**How we’ll support you**:

- Flexible working to assist you balance your personal priorities
- Coaching and support from experts in your team
- A culture of continuous learning to aid progression
- A range of flexible benefits that you can tailor to suit your needs
- Training and development to help you excel in your career

**About us