Data Analyst (AML | Up to 9.7k)

Adecco; Singapore; On-site; SGD 65,000 - 108,000; Full time

Job summary

Adecco is partnering with a leading bank to recruit a Data Analyst (AML) responsible for enhancing customer and payment watchlist screening through data preparation, analysis, and backtesting using Python.

The role focuses on rule design, QA, UAT, and collaboration with Compliance, Technology, and Business units. Ideal candidates have 6+ years of Python data analytics experience and AML domain knowledge. 4-month contract at MBFC in Singapore.

Qualifications

• Proficiency in Python and data analysis on large datasets.
• Experience with AML, screening and backtesting is required.
• Bachelor's degree or equivalent practical experience in a relevant discipline.

Responsibilities

• Extract, parse, cleanse, transform and analyse screening data using Python; identify patterns and gaps.
• Design and backtest enhancements to screening logic and thresholds; present evidence-based recommendations.
• Raise and track Jira items documenting scope, rules, thresholds, and acceptance criteria for implementation; provide analytical clarification during delivery.
• Validate implementation through UAT and post-implementation review; investigate defects and variances.
• Monitor changes and review screening effectiveness; propose tuning and improvements; prepare analytical reports for stakeholders.
• Serve as SME for customer and payment watchlist screening; guide on rules and thresholds.

Skills

Python

Data analysis

Problem solving

Education

Bachelor's degree

Tools

CML

CDSW

Jira

Job description

• We are partnering a leading bank to look for a Data Analyst (AML | Up to 9.7k)

Role Purpose

This hands-on role uses Python and data analytics to enhance customer and payment watchlist screening through data preparation and analysis, investigation, rule design and backtesting, stakeholder recommendations, Technology implementation support, testing, monitoring, reporting, and audit and regulatory RFI support.

Key Responsibilities

3.1 Extract, parse, cleanse, transform and analyse large-scale structured and unstructured screening data using Python to identify patterns, control gaps and optimisation opportunities; investigate missing hits, abnormal hit or alert volumes and unexpected screening outcomes through root-cause and control-impact analysis, remediation and escalation.
3.2 Design and backtest enhancements to screening coverage, matching logic, rules, thresholds and key parameters; analyse viable options and present evidence-based recommendations to Compliance, Technology, Operations and Business stakeholders, including the advantages, disadvantages, expected benefits, detection effectiveness, false-positive rates, operational impact, limitations and residual risk.
3.3 Once approved, raise and track Jira items documenting the screening scope, matching logic, rules, thresholds, key parameters, controls, business requirements and acceptance criteria for Technology implementation; provide analytical clarification during delivery.
3.4 Validate Technology implementation through UAT, parallel-run analysis and post-implementation validation using historical, known-match and edge-case data; compare results with approved requirements and backtesting evidence, investigate defects and material variances, document results, and support formal sign-off.
3.5 Monitor implemented changes and periodically review screening effectiveness and system efficiency; recommend tuning, rule changes or control improvements, provide ad hoc statistical and investigation support, and prepare periodic and ad hoc analytical reports for stakeholders.
3.6 Act as the subject matter expert for customer and payment watchlist screening, providing domain guidance on rules, matching logic, thresholds, testing outcomes and proposed enhancements.
3.7 Support audit and regulatory RFIs, screening-related requests and management queries through targeted data extraction, statistical analysis and timely supporting information; maintain clear documentation linking analysis, recommendations, approvals, implementation requirements and testing results.

Candidate Requirements

4.1 Python and data analysis: Proficiency in Python is mandatory, with proven experience parsing, transforming and analysing large-scale structured and unstructured datasets, identifying patterns, explaining outcomes clearly and working efficiently at scale. Experience with CML or CDSW is required.
4.2 Financial crime risk domain: Practical experience in customer or payment screening, including sanctions and PEP screening, rule tuning, backtesting, UAT and effectiveness testing.
4.3 Bachelor's degree or equivalent practical experience in a relevant discipline, with eight to ten years of relevant experience.
4.4 Strong knowledge of AML/CFT, watchlist screening and name-matching concepts, including exact and fuzzy matching, tokenisation, transliteration, aliases, secondary attributes, thresholds and score adjustments.
4.5 Strong analytical, problem-solving and communication skills, with the ability to explain findings to technical and non-technical audiences, develop evidence-based recommendations and influence stakeholders; able to work independently, manage competing priorities and deliver within required timelines.
4.6 Experience with relational databases, data warehouses and big-data environments.
4.7 Desirable: Knowledge of data pipelines, ETL and data lineage, and experience with Jira or a similar delivery tool.

Technical skills

• Python: 6+ years
• Data Analysis: 6+ years
• Big Data Technologies/Environments: 6+ years
• Data Lineage Analysis: 6+ years
• Data Cleansing & Data Quality Techniques: 4-6 years

MNC/ BANK

The Opportunity

• We are partnering a leading bank to look for a Data Analyst (AML | Up to 9.7k)
• 4 months contract at MBFC