New opportunity

Product & AI Engagement Trainee

Circles · Singapore

About this role

Circles is seeking a curious, analytical, and hands-on graduate to join as a Product & AI Engagement Trainee. This role offers a unique opportunity to work at the intersection of AI, product, and user experience, focusing on turning insights into action and experiments into growth. You will collaborate with stakeholders to understand user behavior, launch experiments, and shape engagement with AI-powered products. Responsibilities include: Boost Engagement & Monetization: Design and run experiments (A/B tests, gamification, AI feature adoption) to improve DAU and MAU; analyze user behavior and campaign performance to identify growth opportunities; collaborate with product and data teams to translate insights into improvements. Accelerate Partner & Content Integration: Support onboarding of new content partners, AI models, and localized experiences; maintain and update the in-app content catalog; coordinate with stakeholders for feature rollouts. Product Operations & Insights: Track product KPIs and compile performance dashboards; document experiment results and recommendations; prepare summaries for internal reviews. User Research & Engagement: Conduct user interviews to gather feedback and test new ideas; develop research scripts; coordinate follow-up surveys to validate hypotheses and measure satisfaction. Requirements: Bachelor’s degree in Business, Engineering, or a related field; strong curiosity about AI, product design, and user engagement; comfort with data and analytics; excellent communication and teamwork skills; organized and proactive. Eligibility: Must be a Singapore Citizen or Permanent Resident; graduated in 2024 or 2025 from Universities, Polytechnics, or ITE (or earlier if completed National Service in 2024/2025). Trainees completing studies in 2025 but receiving certification in 2026 are also eligible.

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

Skills for this role

Product ManagementUser ExperienceA/B TestingData AnalysisUser ResearchGrowth HackingAI Feature AdoptionKPI Tracking

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

Experimentation and Growth Optimization
The role centers on designing and executing A/B tests and gamification strategies to drive DAU and MAU. You must prioritize the ability to translate raw user behavior data into actionable product improvements that directly impact growth metrics.
AI Feature Adoption and Integration
You will be responsible for supporting the onboarding of new AI models and content partners. This requires a focus on maintaining the in-app content catalog and ensuring that new AI-powered features are effectively integrated and adopted by the user base.
User Research and Feedback Synthesis
A core priority is conducting user interviews and managing feedback loops. You must be able to synthesize qualitative user insights into structured recommendations that guide future product design and feature validation.
Product Operations and KPI Tracking
You are expected to maintain performance dashboards and document experiment outcomes. This requires high organizational discipline to ensure that product KPIs are accurately tracked and communicated to stakeholders for internal reviews.

Likely challenges to think through

Balancing Qualitative Insights with Quantitative Data
You will face the challenge of reconciling subjective user interview feedback with objective A/B test results. Prepare by practicing how to build a cohesive narrative that uses both data types to justify product decisions.
Stakeholder Coordination in a Fast-Paced Environment
The role involves collaborating with product, data, and external partners. The difficulty lies in managing competing priorities and timelines. Prepare by demonstrating your ability to proactively communicate status updates and manage expectations across diverse teams.
Navigating AI Feature Complexity
AI features can be unpredictable in user adoption. You may struggle to isolate variables in experiments when AI models are involved. Prepare by researching methodologies for testing AI-driven user experiences and how to measure 'engagement' specifically for AI tools.

Practice your answers

Technical practice

Designing an A/B Test for AI Feature Adoption

What to coverExplain the steps you would take to design an A/B test for a new AI-powered feature. Focus on defining the hypothesis, selecting the primary metric (e.g., DAU/MAU impact), determining the sample size, and ensuring statistical significance.

Interpreting Product KPIs

What to coverIf you notice a sudden drop in Daily Active Users (DAU), what metrics would you investigate to diagnose the cause? Suggest a structured approach to analyzing user behavior data to identify if the issue is technical, content-related, or a shift in user engagement.

Conducting User Research for New Features

What to coverDescribe your process for creating a user research script to test a new product idea. How do you ensure your questions are unbiased and effectively capture actionable qualitative feedback?

Managing Content Catalog Updates

What to coverHow would you prioritize updates to an in-app content catalog to ensure it remains relevant to users? Discuss the balance between data-driven insights and qualitative user feedback when deciding what content to promote or remove.

Translating Data Insights into Product Improvements

What to coverProvide an example of how you would present experiment results to stakeholders. How do you ensure your recommendations are clear, evidence-based, and aligned with overall product growth goals?

Growth Hacking Fundamentals

What to coverWhat are the key differences between acquisition and engagement strategies in a digital product? Suggest how you might apply gamification to improve long-term retention for an AI-integrated mobile app.

Onboarding New Partners and AI Models

What to coverWhen supporting the integration of a new AI model, what operational steps are necessary to ensure a smooth rollout? Consider the importance of documentation, stakeholder coordination, and post-launch performance tracking.

Validating Product Hypotheses

What to coverAfter conducting user interviews, how do you synthesize the findings to validate or invalidate a product hypothesis? Explain the importance of identifying recurring themes and how to document these for the product team.

Behavioural practice

Handling Cross-Functional Collaboration

What to coverDescribe a time when you had to work with multiple teams to complete a project. How did you manage competing priorities and ensure clear communication between stakeholders?

Taking Ownership of a Project

What to coverTell me about a time you took the initiative to solve a problem or improve a process without being explicitly asked. What was the situation, and what was the outcome?

Adapting to Ambiguity

What to coverProduct roles often involve shifting priorities. Describe a situation where you had to adapt to a sudden change in project direction. How did you stay organized and maintain your focus?

Learning from Failure

What to coverDescribe a time when an experiment or project did not yield the results you expected. What did you learn from the process, and how did you document those findings for future use?

Sharpen your knowledge

Topics to prepare

Product Experimentation Frameworks
The role requires designing and running A/B tests and gamification experiments to drive DAU/MAU. Exercise: Research the scientific method for product experimentation, specifically how to formulate a hypothesis, define success metrics, and determine sample sizes for A/B tests.
AI Feature Adoption Lifecycle
You will be shaping how users engage with AI-powered products. Exercise: Study the typical user journey for AI features, focusing on onboarding, value realization, and retention strategies to identify why users might drop off or fail to adopt new AI tools.
User Research Methodologies
The role involves conducting interviews and developing research scripts. Exercise: Practice drafting a semi-structured interview guide for a hypothetical mobile app feature, focusing on open-ended questions that uncover user pain points rather than leading questions.

Skills to revise

Data-Driven Decision Making
You must track KPIs and compile performance dashboards. Exercise: Create a mock dashboard using spreadsheet software that tracks key growth metrics like DAU, MAU, and conversion rates, then write a brief summary explaining one potential growth opportunity based on the data.
Product Documentation
The role requires documenting experiment results and recommendations. Exercise: Write a one-page 'Experiment Post-Mortem' template that includes the original hypothesis, the test design, the actual results, and three actionable recommendations for the next iteration.

Your preparation checklist

Analyze Circles' product ecosystem
Research the Circles.Life, Circles Aspire, and Jetpac brands to understand their digital telco and SaaS offerings. Prepare a brief summary of how these products might integrate AI to improve user engagement or monetization.
Draft an A/B testing framework
Create a mock experiment plan for a hypothetical AI feature in a mobile app. Define the objective (e.g., increasing DAU), the hypothesis, the metrics to track, and how you would analyze the results to inform future product iterations.
Prepare a user research script
Develop a structured interview guide for gathering feedback on a new AI-powered feature. Include open-ended questions designed to validate user needs, identify pain points, and measure overall satisfaction.
Build a sample performance dashboard
Using a tool like Excel or Google Sheets, create a mock dashboard that tracks key product KPIs such as DAU, MAU, and feature adoption rates. Include a section for documenting actionable insights derived from the data.
Formulate strategic questions for the interview
Prepare thoughtful questions regarding how the team balances AI innovation with user experience, the current challenges in content integration, or how the company prioritizes experiments based on data insights.

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