The questions below represent the patterns and themes commonly experienced by candidates interviewing for the Data Scientist role at Airwallex Pty. Use these to guide your practice, focusing on your underlying methodology rather than memorizing specific answers.

SQL and Data Manipulation

These questions test your ability to write efficient, accurate code under pressure to solve realistic data extraction problems.

• Write a SQL query to calculate the 7-day rolling average of transaction volumes per user.
• Given a table of user events, write a query to find the time difference between a user's first login and their first successful transaction.
• Write a Pandas script to merge two large datasets, handle missing values in a specific column, and aggregate the total revenue by region.
• How do you optimize a SQL query that uses multiple window functions and is currently timing out?
• Write a query to identify users who have made transactions in more than three different currencies within a single week.

Experimentation and Statistics

Interviewers use these questions to gauge your statistical rigor and your ability to design valid product experiments.

• How would you design an A/B test to evaluate a new onboarding flow for enterprise clients?
• What would you do if the results of an A/B test show a statistically significant positive result, but the sample size was smaller than originally planned?
• Explain the concept of network effects in A/B testing. How would you handle this if testing a peer-to-peer transfer feature?
• How do you determine the minimum detectable effect (MDE) before launching an experiment?
• If multiple experiments are running on the same checkout page simultaneously, how do you ensure the results are valid?

Business Case Studies and Metrics

These open-ended questions test your product intuition, structured thinking, and ability to handle ambiguity.

• Our overall transaction success rate has decreased by 2% this week. Walk me through your diagnostic process.
• We are launching a new corporate card product. What are the top three metrics you would track to measure its success?
• A product manager suggests using "total clicks on the transfer button" as the primary metric for a new feature. Is this appropriate? Why or why not?
• How would you segment our user base to identify the most profitable cohorts for a targeted marketing campaign?
• Walk me through how you would estimate the impact of reducing our FX conversion fee by 10 basis points.

Machine Learning and Modeling

These questions assess your foundational knowledge of predictive modeling and how to apply it to business use cases.

• Walk me through the end-to-end process of building a model to predict transaction fraud.
• How do you handle missing or highly skewed data before training a classification model?
• Explain how a Random Forest algorithm works to a non-technical product manager.
• What metrics would you use to evaluate a model that predicts a very rare event, such as a high-value account takeover?
• How would you determine if a deployed machine learning model is degrading over time?

Be ready to go over:

• Root Cause Analysis – Investigating sudden drops or spikes in key business metrics.
• Feature Evaluation – Assessing the potential impact of a proposed product change.
• Business Process Alignment – Tying data science solutions directly to revenue, cost savings, or user growth.
• Advanced concepts (less common) – Market expansion sizing, pricing strategy analytics, and unit economics modeling.

Example questions or scenarios:

• "Our cross-border transaction success rate dropped by 5% yesterday. Walk me through how you would investigate this."
• "We want to introduce a new fee structure for currency conversion. How would you analyze the potential impact on user retention?"
• "Walk me through the lifecycle of a global payment and identify where data science can optimize the process."

Machine Learning Foundations

While this role leans heavily toward analytics and product, you are still expected to understand core Machine Learning principles. Interviewers will test your grasp of fundamental algorithms and how to apply them to business problems, rather than asking you to write complex neural networks from scratch.

Be ready to go over:

• Supervised Learning – Classification and regression models (e.g., Logistic Regression, Random Forests, XGBoost).
• Model Evaluation – Precision, recall, F1-score, ROC-AUC, and understanding trade-offs in imbalanced datasets (crucial for fraud detection).
• Feature Engineering – Selecting and transforming variables to improve model performance.
• Advanced concepts (less common) – Unsupervised learning (clustering for customer segmentation), time-series forecasting, and model deployment pipelines.

Example questions or scenarios:

• "Explain the bias-variance tradeoff and how it applies to a model predicting transaction fraud."
• "How would you build a model to predict which users are most likely to churn in the next 30 days?"
• "What evaluation metrics would you use for a highly imbalanced dataset, and why?"

6. Key Responsibilities

As a Data Scientist at Airwallex Pty, your day-to-day work will be a dynamic mix of deep technical execution and strategic business partnering. You will spend a significant portion of your time querying large-scale relational databases to extract insights regarding user behavior, transaction flows, and product performance. You will be responsible for building and maintaining the foundational data pipelines and dashboards that product managers and operations teams rely on daily.

Beyond reporting, you will actively drive the company's experimentation culture. You will design A/B tests, establish tracking requirements for new feature launches, and present post-experiment deep dives to leadership. Your role involves taking raw experimental data, applying statistical rigor, and translating the results into clear "ship or do not ship" recommendations.

Collaboration is a massive part of this role. You will work hand-in-hand with engineering teams to ensure data logging is accurate and with product leaders to shape the roadmap. You will frequently be tasked with investigating ambiguous business problems—such as unexpected shifts in payment success rates—requiring you to dive deep into the data, build predictive models where necessary, and present your findings confidently to stakeholders.

7. Role Requirements & Qualifications

To be a competitive candidate for the Data Scientist position at Airwallex Pty, you must bring a strong mix of analytical rigor, coding proficiency, and business intuition. The team looks for individuals who are self-starters and can operate independently in a fast-paced environment.

• Must-have skills – Expert-level SQL for complex data extraction.
• Must-have skills – Strong proficiency in Python, specifically utilizing Pandas for data manipulation and analysis.
• Must-have skills – Deep understanding of A/B testing, statistical significance, and experimental design.
• Must-have skills – Exceptional communication skills, with the ability to translate technical findings into business strategy.
• Nice-to-have skills – Prior experience in FinTech, specifically dealing with cross-border payments, FX, or fraud detection.
• Nice-to-have skills – Experience building and deploying foundational machine learning models (e.g., XGBoost, Random Forest).
• Nice-to-have skills – Familiarity with modern data stack tools (e.g., Snowflake, dbt, Looker, or Tableau).

8. Frequently Asked Questions

Q: How much domain knowledge in FinTech or payments is actually required? While you do not need to be an absolute expert in global finance, interviewers at Airwallex Pty expect you to understand the basic mechanics of cross-border payments, FX, and B2B financial products. Familiarizing yourself with their core business model will significantly improve your performance in the case study rounds.

Q: Are the coding rounds done on a whiteboard or a shared IDE? You will typically use a shared, web-based coding environment (like CoderPad) for the SQL and Python/Pandas rounds. You are expected to write syntactically correct code, so practice coding without the heavy assistance of advanced auto-complete tools.

Q: What is the culture like during the interview process? The culture is fast-paced, direct, and heavily data-driven. Interviewers value candidates who are concise, confident, and able to defend their analytical choices. Do not be surprised if interviewers push back on your assumptions; they are testing your resilience and logical reasoning.

Q: How should I handle an interviewer who seems impatient or provides ambiguous prompts? Stay calm and structured. Ambiguity is often intentional to see how you define scope. Proactively state your assumptions, define your metrics clearly, and ask targeted, specific questions rather than broad ones to keep the conversation moving constructively.

Q: How heavily weighted are the behavioral rounds compared to the technical rounds? Both are critical. You cannot pass the loop without strong SQL and analytical skills, but Airwallex Pty also heavily weights the final rounds to ensure you have the soft skills necessary to influence stakeholders and drive cross-functional projects.

9. Other General Tips

• Clarify Aggressively and Early: Case studies will lack initial detail. Before you start solving, spend two minutes outlining the business goal, the user, and your assumptions. Get the interviewer to agree with your setup before moving forward.
• Think Out Loud During Pandas Coding: When writing Python code, narrate your thought process. If you forget a specific Pandas syntax, explain what you are trying to achieve conceptually; interviewers will often guide you if your logic is sound.
• Master the "Why" Behind Metrics: Do not just list generic metrics (like DAU or Revenue). Always explain why a specific metric is the most appropriate measure of success for the specific Airwallex Pty feature being discussed.