Risk analytics analyst

What does a risk analytics analyst do?

A risk analytics analyst builds and runs quantitative models and data analysis that help a firm measure risk and meet compliance rules.

In Singapore, most work in banks, insurers, asset managers and payment firms.

The role usually sits in a risk, compliance or data team. You report to a risk analytics manager or a head of risk. Some analysts specialise in one line of business, such as retail banking or asset management.

You work with risk and compliance officers, business units, data engineers, IT teams and model validators. Your outputs also reach auditors and, indirectly, the Monetary Authority of Singapore (MAS), which supervises financial institutions.

What the work involves

• Build and update risk models, such as credit scoring, pricing or valuation models, for a business unit.
• Pull and clean data from internal systems, then test it for gaps and errors before you use it.
• Write reports and dashboards that show risk trends, benchmarks and model results.
• Meet business and compliance teams to turn their questions into clear analytical requirements.
• Document model assumptions and test results so validators and auditors can follow your work.
• Explain findings simply to people who do not work with statistics.

Pay in Singapore

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Skills a risk analytics analyst needs

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Risk analytics analyst job description

We are hiring a risk analytics analyst to join our risk and compliance team in Singapore.

You will develop and run quantitative models, analyse risk data and prepare reports that support daily risk and compliance decisions. You will work with business units, data teams and model validators. You will also document your work so that auditors and regulators can review it.

Responsibilities

• Develop, test and maintain quantitative models for credit, market, operational or ESG-related risk.
• Collect, clean and combine data from several internal and external sources.
• Analyse trends and run baseline comparisons to spot changes in risk levels.
• Build dashboards and reports for risk managers, compliance officers and senior management.
• Gather and record business requirements for risk and compliance analytics.
• Write model documentation, including assumptions, limits and test results.
• Support model validation, internal audit and regulatory reviews with data and explanations.
• Help deploy models and analytics tools into the firm's systems and keep their documentation current.
• Train colleagues to use analytical tools and read model outputs correctly.
• Present findings and recommendations to non-technical stakeholders.

Requirements

• A degree in statistics, mathematics, economics, finance, computer science, data science or a related field.
• 0 to 3 years of experience in risk, compliance, analytics or a quantitative role. Graduates with strong project work are often considered.
• Working skill in SQL and at least one of Python or R, plus advanced Excel.
• Sound grounding in statistics, such as regression, hypothesis testing and model diagnostics.
• Knowledge of financial products and the rules that apply to financial institutions in Singapore is a plus.
• Clear written and spoken communication, including the ability to explain results to non-specialists.

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Risk analytics analyst career path

Most risk analytics analysts start in a general analyst role, move into a business line, and then take on model ownership or team leadership.

The official framework lists the same role across asset management, corporate, retail and investment banking.

1. Risk analytics analyst

   Entry · SkillsFuture title

   You build and test models, prepare data and write reports under the guidance of a senior analyst or manager.

2. Risk analytics analyst in a business line

   Mid · SkillsFuture title

   You specialise in one area, such as asset management, corporate banking, retail banking or investment banking, and own models for that business.

3. Senior risk analytics analyst

   Senior

   You lead complex modelling work, review the work of others and advise business units on methods and results.

4. Risk analytics manager

   Lead

   You run a team, set modelling standards, manage projects and report to the head of risk or compliance.

Steps marked "SkillsFuture title" follow the career pathway in the SkillsFuture Skills Framework. The others are titles commonly used in Singapore.

Related jobs and their pay

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Risk analytics analyst interview questions

We wrote these questions from the skills this job needs. They are not an official question bank.

1. How would you build a logistic regression model to estimate probability of default, and how would you judge it?

   What a good answer covers

   • Defines the target, such as 90 days past due within 12 months.
   • Explains variable selection, binning and checks for multicollinearity.
   • Measures ranking power with AUC, Gini or KS.
   • Checks calibration so predicted rates match observed defaults.
   • Tests on out-of-time data, not only a random split.

2. How do you monitor a live risk model for drift?

   What a good answer covers

   • Compares input and score distributions over time, for example with population stability index.
   • Back-tests predictions against actual outcomes.
   • Sets thresholds that trigger a review.
   • Escalates results to the model owner and validator.
   • Keeps a record of each check.

3. Write a SQL query to find customers who had a loan more than 90 days overdue in the last 12 months. What would you check before trusting the result?

   What a good answer covers

   • Joins the customer and repayment tables on the correct key.
   • Filters by date and groups by customer.
   • Uses a window function or subquery where needed.
   • Checks for duplicate rows and null dates.
   • Reconciles the total against a known report.

4. Walk us through a credit risk model you built in Python from raw data to a result.

   What a good answer covers

   • Describes cleaning and feature work in pandas.
   • Splits data by time to avoid leakage.
   • Names the model and library, such as statsmodels or scikit-learn.
   • Explains how the code was made repeatable and version-controlled.
   • States the limits of the model.

5. What is Value at Risk, and where does it fall short?

   What a good answer covers

   • Defines it as a loss level not expected to be exceeded at a set confidence over a set period.
   • Names historical, parametric and Monte Carlo methods.
   • Explains that it says little about losses beyond the cut-off.
   • Mentions back-testing and stress tests as checks.
   • Notes the effect of the data window chosen.

6. When is Excel enough for a risk analysis, and when should you move to Python or R?

   What a good answer covers

   • Uses Excel for small, one-off checks and quick stress tests.
   • Points to weaknesses such as manual errors, weak version control and poor audit trails.
   • Moves to code when data is large or the work must be repeated.
   • Keeps clear labels and separates inputs from formulas.

7. Default events are rare in your data set. How do you handle that?

   What a good answer covers

   • Checks the base rate before choosing a method.
   • Considers class weights or resampling, and explains the risks of each.
   • Uses metrics suited to imbalance, such as precision-recall and KS.
   • Avoids reporting accuracy alone.
   • Handles missing values and outliers with a documented rule.

8. Your team uses both R and Python. How do you decide which to use for a task?

   What a good answer covers

   • Considers team standards and who will maintain the code.
   • Names strengths of each, such as R for some statistical and time series packages.
   • Weighs how the output will be deployed.
   • Keeps the work reproducible whichever tool is used.

9. How would you use ChatGPT in your work, and what would you never do with it?

   What a good answer covers

   • Uses it for drafting code, explaining methods or tidying text.
   • Never pastes customer data or confidential model details into a public tool.
   • Tests and reviews all generated code before use.
   • Follows the firm's policy on AI tools.
   • Takes responsibility for the final result.

1. A model validator finds that your model performs worse on recent data than on the build sample. What do you do?

   What a good answer covers

   • Reproduces the finding before arguing.
   • Looks for causes, such as data change, a shift in customer mix or leakage.
   • Proposes a fix or a temporary adjustment.
   • Updates the documentation and tells the model owner.
   • Agrees a timeline with the validator.

2. Your risk dashboard shows a 30 per cent drop in exposure overnight. What are your first steps?

   What a good answer covers

   • Checks the data feed and load logs before the model.
   • Compares row counts and totals with the source system.
   • Contacts the data engineering team.
   • Tells users the figures are under review.
   • Records the cause and adds a check to prevent a repeat.

3. You must present an ESG-related risk analysis to a committee with no technical background. How do you prepare?

   What a good answer covers

   • Starts with the decision the committee must make.
   • Uses a few clear charts, not model detail.
   • Explains assumptions and uncertainty without jargon.
   • Keeps technical results in an appendix.
   • Prepares for likely questions.

1. Tell us about a time you found an error in your own analysis after you shared it.

   What a good answer covers

   • Describes the error honestly and quickly.
   • Tells the affected people at once.
   • Explains how the fix was made and checked.
   • Shows what check was added afterwards.

2. Describe a time you disagreed with a business stakeholder over a model assumption.

   What a good answer covers

   • Explains both views with evidence.
   • Tests the assumption with data where possible.
   • Stays calm and keeps the discussion on facts.
   • Escalates or documents the outcome if no agreement is reached.

1. How would you document and test a model so it meets MAS expectations for model governance?

   What a good answer covers

   • Records purpose, data, method, assumptions and limits.
   • Separates the build team from independent validation.
   • Sets ongoing monitoring and a review schedule.
   • Keeps version history and approval records.
   • Checks the firm's current MAS requirements with the compliance team.

2. You want to use customer data for a new analytics project. What does the Personal Data Protection Act (PDPA) mean for you?

   What a good answer covers

   • Checks that the use fits the purpose the customer agreed to or another valid basis.
   • Uses only the data the project needs.
   • Masks or removes identifiers where possible.
   • Limits access and protects the data in storage and transfer.
   • Asks the data protection officer before starting.

Common questions about a risk analytics analyst job

Sources