Editorial summary

The company is seeking a Data Analyst within the Banking sector to support the Legal, Compliance & Secretariat group by evaluating watchlist screening and matching systems for customer and payment screening, including sanctions and PEP checks. The role involves data analysis, backtesting, and developing tuning and rule proposals to enhance system effectiveness while assessing associated risks. Key responsibilities include parsing, transforming, and analyzing both structured and unstructured screening data. The position offers a salary of up to $8,000.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job https://singapore.job-q.com/jobs/detail/data-analyst-banking-up-to-8k-34189 first appeared in Job-Q.com on 24 Sep 2026]

Business Function

• Group Legal, Compliance & Secretariat protects the bank’s reputation, capital, and stakeholder trust.

• This hands-on analytics role evaluates watchlist screening and matching systems used for customer and payment screening, including sanctions and PEP screening.

• The role applies data analysis and backtesting to develop tuning and rule proposals, assess their effectiveness and risk implications, and support stakeholder approval.

Key Responsibilities

• Parse, transform, and analyse structured and unstructured screening data, including customer, identity, watchlist, payment-message, and transaction-party data, as applicable.

• Compare new and existing watchlist screening and matching systems using defined datasets, scenarios, and effectiveness metrics.

• Analyse differences in match generation, detection coverage, false-positive rates, data handling, and processing performance, and identify their root causes.

• Develop tuning strategies and propose screening rules, parameters, or configuration changes based on analytical evidence.

• Backtest proposed changes using historical and edge-case data to assess detection effectiveness, false-positive rates, operational impact, and residual risk.

• Prepare evidence-based proposals for stakeholder approval, documenting the methodology, assumptions, options, expected benefits, limitations, operational impact, residual risk, recommendation, and rationale.

• Define analytical datasets, test scenarios, data quality controls, and acceptance measures for comparative assessment.

• Maintain reproducible analyses and clear traceability from source data and methods through tests, results, proposals, and decisions.

• Develop dashboards and reports covering match volumes, false-positive rates, detection coverage, and processing performance.

• Ensure analyses and proposals meet internal standards and are suitable for Compliance and audit review.

Candidate Requirements

• Bachelor’s degree or equivalent experience in Computer Science, Statistics, Engineering, Data Science, or another quantitative or technical field.

• More than five years’ relevant experience, with strong financial crime prevention and data analytics expertise.

• Proficiency in Python is mandatory. Experience with enterprise analytical platforms such as CML or CDSW, and with big data or data warehousing, is desirable.

• Proven experience parsing, transforming, and analysing large-scale structured and unstructured datasets is mandatory. Candidates must be able to identify patterns, explain outcomes, and work efficiently at scale.

• Experience in screening-system comparison, tuning, rule development, and backtesting is required, including assessment of detection effectiveness, operational impact, and residual risk.

• Strong knowledge of AML/CFT, watchlist screening, and name-matching concepts, including sanctions and PEP screening. Experience in customer or payment screening, and familiarity with the relevant data and matching attributes, is desirable.

• Strong analytical, problem-solving, critical-thinking, and data-visualisation skills, with attention to data quality and reproducibility.

• Ability to develop, document, and present clear, evidence-based proposals that balance detection benefits against false-positive rates, operational impact, limitations, and residual risk for stakeholder approval.

• Experience using Jira or similar tools to manage requirements, test cases, and defects is desirable.
  

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