New opportunity

Risk Services - AI Factory Data Scientist Off-Cycle Internship (Jan - Jun 27)

PricewaterhouseCoopers Risk Services Pte. Ltd. · Singapore

About this role

This off-cycle Data Scientist internship in PwC Risk Services’ AI Factory is based at Singapore - Marina One and runs from January to June 2027. The AI Factory collaborates across PwC’s network and territories to develop AI solutions for client service delivery. The role sits within Risk Services, which helps clients assess and manage business risks.

Responsibilities

Develop, use and maintain generative AI models and other data analysis techniques to collect and explore data and extract insights.
Support risk-management administrative tasks through communication and collaboration with stakeholders.

Requirements

A degree in a quantitative field, such as Data Science, Computer Science, Applied Statistics or Mathematics.
Proficiency in Python.

The posting also lists optional skills spanning AI implementation, machine learning, deep learning, natural language processing, data infrastructure and modeling, programming, and communication. The position is listed as full-time, with 0% travel. Work visa sponsorship is not available, and government clearance is not required. Applicants are asked to upload a resume and academic transcript or degree audit in PDF format and indicate their first-choice role and, where applicable, a second choice.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonGenerative AIData AnalysisRisk ManagementCommunicationStakeholder CollaborationAI ImplementationC++Data InfrastructureData IntegrationData ModelingData PipelinesData QualityDeep LearningGPU ProgrammingJavaMachine LearningNamed Entity RecognitionNatural Language ProcessingNatural Language ToolkitActive Listening

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Develop and maintain generative AI models
The internship explicitly includes developing, using and maintaining generative AI models. Prepare to explain a small model workflow from initial approach through evaluation and upkeep, distinguishing what you built from what you maintained.
Extract defensible insights from data
The role calls for collecting and exploring data, applying other analysis techniques, and extracting insights. Python proficiency is required. Prepare to show how you check data, choose an analysis and explain what the results do—and do not—support.
Coordinate risk-management work
The posting assigns risk-management administrative tasks that require communication and collaboration with stakeholders. Prepare an example of tracking requests, clarifying responsibilities and conveying progress clearly.
Connect technical work to client delivery
The AI Factory’s stated mission is to collaborate across PwC’s network and territories on AI solutions for client service delivery. Prepare to explain the practical use of an analysis or model without claiming that interns own cross-territory projects.

Likely challenges to think through

Speed versus dependable outputs
A plausible challenge, not a stated employer problem: the AI Factory’s aim to build solutions faster could pull against the need to maintain models and verify insights. Prepare to discuss a proportionate way to test outputs, record limitations and decide when an approach needs revision.
Useful insights versus risk-sensitive data handling
Risk Services examines areas including data security and privacy, while this role collects and explores data. The posting does not specify the intern’s datasets; prepare to explain how you would seek guidance on data access and handle uncertainty without overstating conclusions.
Technical work versus coordination time
The role combines model and analysis work with stakeholder-facing risk-management administration. A likely trade-off is protecting time for analysis while keeping requests and updates moving. Prepare an example of prioritising tasks, confirming expectations and escalating a blocker.

Practice your answers

Technical practice

How would you use Python to explore an unfamiliar dataset for risk-related insights?

What to coverThe posting requires Python proficiency and describes collecting, exploring and extracting insights from data. Suggested preparation: explain how you would inspect structure, summarize patterns and check whether an apparent finding is supported by the data.

What data-quality issues would you check before drawing conclusions?

What to coverData quality is listed as an optional skill, and the role involves data analysis. Suggested preparation: discuss missing values, duplicates and inconsistent definitions, including when to correct an issue and when to disclose its effect on the findings.

When would you use a generative AI model rather than another analysis technique?

What to coverThe internship involves generative AI models and other data analysis techniques. Suggested preparation: compare the approaches against the type of data, the insight needed and the importance of verifiable results.

How would you assess whether a generative AI output is useful and reliable?

What to coverThe role includes using generative AI to extract insights for an AI Factory focused on client service delivery. Suggested preparation: describe checking outputs against source data, examining errors and stating limits rather than treating fluent text as evidence.

How would you investigate a generative AI model whose outputs have become less useful?

What to coverMaintaining generative AI models is an advertised responsibility. Suggested preparation: explain how you would compare current and earlier outputs, inspect changes in input data and isolate a possible cause before proposing a fix.

How would you handle sensitive data while exploring a risk-related dataset?

What to coverThe Risk Services description mentions data security and privacy among the areas it examines. Suggested preparation: discuss limiting exposure of sensitive information and checking applicable data-handling constraints before using data with an AI model.

How might you extract useful information from unstructured text?

What to coverNatural language processing and named entity recognition appear in the optional skills list. Suggested preparation: outline a possible text-analysis approach, how you would check extraction errors and when simpler analysis might suffice.

How would you make a repeatable analysis dependable when new data arrives?

What to coverThe role involves collecting and exploring data, while data pipelines and integration are listed as optional skills. Suggested preparation: explain checks on incoming data, reproducible Python steps and how you would flag a changed data definition.

How would you distinguish a meaningful risk-related pattern from a misleading one?

What to coverRisk Services uses data analytics to help clients assess risks, and the internship involves extracting insights. Suggested preparation: discuss comparison groups, uncertainty and plausible alternative explanations before communicating a conclusion.

Behavioural practice

Tell me about a time you coordinated a task with several stakeholders.

What to coverThe internship includes risk-management administrative tasks that require communication and collaboration. Suggested preparation: use a real situation, your actions to clarify responsibilities or follow up, and the resulting outcome.

Describe a time you explained an analytical finding to someone with a different background.

What to coverThe role combines data insights with stakeholder communication. Suggested preparation: give a real situation, show how you adapted your explanation and conveyed any uncertainty, and state the result.

Tell me about a time feedback changed your technical work.

What to coverAccepting feedback is listed as an optional skill, and the internship includes developing and maintaining models. Suggested preparation: describe the situation, what you changed in response and the result, without implying the posting specifies a particular review process.

Describe a time you collaborated across teams with different priorities.

What to coverThe AI Factory’s stated mission involves collaboration across PwC’s network and territories, though the posting does not assign a specific cross-territory project to this intern. Suggested preparation: use a real situation, your actions to resolve differing needs and the outcome.

Sharpen your knowledge

Topics to prepare

Generative AI model lifecycle
The role involves developing, using and maintaining generative AI models. Suggested revision exercise: sketch how you would test a model’s outputs, investigate a poor result and decide whether an update helped.
Data analysis and quantitative reasoning
The role calls for collecting and exploring data to extract insights, and asks for a quantitative degree. Suggested revision exercise: inspect a small dataset, choose a useful summary and explain what conclusion the data cannot support.
Risk Services context
Risk Services considers areas including cybersecurity, supply chains, data security and privacy. Suggested revision exercise: pick one area and frame a question data analysis could inform, noting a limitation. This is context for preparation, not a stated domain-expertise requirement.
Stakeholder collaboration
The internship includes risk-management administrative tasks and communication with stakeholders. Suggested revision exercise: plan how you would clarify an ambiguous request, confirm an owner and report progress without overstating an analysis result.

Skills to revise

Python data exploration
Python proficiency is explicitly requested. Suggested revision exercise: write a Python script that loads a small dataset, checks missing values, computes summaries and prints a short account of the findings.
Generative AI output checks
The role involves using and maintaining generative AI models. Suggested revision exercise: create a few sample inputs and expected-output criteria, run them against a model you can access, and record failures and changes. This workflow is preparation advice, not a specified employer process.
Data-quality checks
Data quality is listed as an optional skill, and the role involves collecting and exploring data. Suggested revision exercise: add Python checks for duplicates, missing fields and inconsistent values, then explain how each issue could affect an insight.
Concise stakeholder update
The posting explicitly mentions communication and collaboration for risk-management administrative tasks. Suggested revision exercise: draft a brief update stating the request, work completed, an unresolved question and the next action.

Your preparation checklist

Map your evidence to the stated requirements
Prepare a brief account of your quantitative degree or studies and specific examples of Python work. The posting explicitly asks for a quantitative-field degree and Python proficiency.
Prepare a data-to-insight walkthrough
Choose a project you can discuss, or label a practice example as hypothetical. Show how you collected or explored data, used Python to analyze it, and checked whether the resulting insight was sound; these activities reflect the stated responsibilities.
Explain a generative AI example
If you have a relevant project, prepare a concise walkthrough of the model’s purpose, inputs, outputs, evaluation and maintenance. Otherwise, prepare a clearly hypothetical example; the role includes developing, using and maintaining generative AI models.
Practice a risk-services design exercise
As interview preparation, sketch a hypothetical AI tool that extracts insights for a client risk question. State what data it would need, how you would check output quality, and what privacy or data-security concerns you would raise; this is a suggested exercise, not an advertised assignment.
Prepare a stakeholder communication example
Outline an example—or a hypothetical response—showing how you would clarify a request, share progress and track an administrative task with stakeholders. The posting explicitly includes collaboration and risk-management administrative work.
Draft focused questions for the team
Prepare questions about the kinds of data the intern would explore, how AI Factory work supports client service delivery, and how the intern would collaborate with stakeholders. These are questions to ask, not assumptions about the team’s process.
Assemble the requested application materials
Prepare your resume and academic transcript or degree audit as PDFs, and decide which role to name as your first choice and, if applicable, second choice, as the posting requests.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…