Business Analyst (Banking/Python)

PERSOL SINGAPORE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

PERSOL SINGAPORE PTE. LTD. is seeking an experienced analytics professional for Group Legal, Compliance & Secretariat to evaluate watchlist screening and matching systems, including sanctions and PEP screening. You will apply data analysis and backtesting to tune rules and support stakeholder approvals.

The role requires 5+ years in financial crime prevention with Python expertise; strong data visualization and reproducible analyses, plus comfort with Jira and enterprise analytics platforms.

Qualifications

• Bachelor's degree or equivalent in Computer Science, Statistics, Engineering, Data Science, or another quantitative 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.
• 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.
• Experience using Jira or similar tools to manage requirements, test cases, and defects is desirable.

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.

Skills

Python

Data analytics

AML/CFT

Watchlist screening

Name matching

Backtesting

Jira

Big Data

Data warehousing

Education

Bachelor's degree in Computer Science / Statistics / Engineering / Data Science

Tools

CML

CDSW

Job description

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.