Data Analyst (Banking / up to $8K)

PERSOL SINGAPORE PTE. LTD.; Singapore; Sur place; SGD 90 000 - 150 000; Plein temps

Résumé du poste

PERSOL Singapore Pte Ltd is seeking a hands-on analytics professional to evaluate watchlist screening and matching systems for customer and payment screening. The role applies data analysis and backtesting to tune rules and assess risk, with stakeholder alignment across Compliance.

You will develop datasets, perform backtests, and create dashboards showing detection coverage, false positives, and processing performance. Strong Python and AML knowledge required.

Qualifications

• Bachelor’s degree or equivalent in a quantitative field.
• 5+ years in financial crime analytics and data science.
• Proficiency in Python mandatory; experience with big data platforms desirable.
• Experience in screening systems, tuning, and backtesting.
• Strong knowledge of AML/CFT and sanctions/PEP screening.

Responsabilités

• Parse, transform, and analyse screening data, including customer and transaction data.
• Compare watchlist systems using defined datasets, scenarios, and metrics.
• Analyse differences in match generation, coverage, false positives, and performance.
• Develop tuning strategies and propose screening rules based on evidence.
• Backtest changes to assess detection, FP rate, and residual risk.
• Prepare evidence-based proposals for stakeholder approval with rationale.
• Define datasets, test scenarios, data quality controls, and acceptance measures.
• Maintain reproducible analyses with traceability from data to decisions.
• Develop dashboards and reports on match volumes, FP rates, and performance.
• Ensure analyses meet internal standards for Compliance and audit review.

Connaissances

Python

Data analytics

AML/CFT concepts

Large-scale data analysis

Problem solving

Formation

Bachelor’s degree in CS/Statistics/Engineering/Data Science

Outils

Jira

Description du poste

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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PERSOL Singapore Pte Ltd • RCB No. 200007268E • EA License No. 01C4394 • EA Registration No. R1877971 (Derrick Tiew Yong Han)