Here is what we will be assessing:

• Analysis: Quality and depth of insights, as well as your technical approach
  
• Problem solving: Customer-centric recommendations with clear, well-reasoned methods 
  
• Presentation and communication: Aim for a clear structure with engaging visuals, and concise delivery
  

Important details: You have 7 days to complete this task. We recommend spending around 2 hours on it, and ask that you don’t exceed a total of 3 hours.

Here are some tips and advice to help you succeed in your Data Analyst take-home assignment for Wise, especially in the context of Fintech. Suggested time allocations for each part are included to help with planning:

• Research Wise: As we are a fintech company specialising in cross-border money transfers, focus on data points relevant to the finance sector, such as transaction data, fees, customer behaviour, and currency exchange
• Consider key questions: Are there trends in transaction volume? How can customer satisfaction be improved? What are the key factors impacting transfer success rates or fees?

• Read the instructions carefully to understand what's expected. Are they asking for exploratory data analysis (EDA), insights, visualisations, or maybe predictive modelling?
• If the problem is vague, don’t hesitate to make assumptions—just be sure to clearly state them in your report or analysis
  

Step-by-step process:

• Plan out your workflow for efficiency and structure, including data cleaning, EDA, and visualisations
• Map out focus areas based on task priorities

• Ensure the data is free of nulls, duplicates, and outliers
• Perform initial checks on data structure and types

• Conduct summary statistics, correlation checks, and distribution analyses to identify trends and anomalies
• Note any insights that could lead to actionable recommendations

• Create 2-3 clear visualisations (using charts, graphs, or heat maps) that highlight the most relevant findings
• Keep in mind the audience, as technical and non-technical stakeholders may be reviewing your report

• Focus on key business insights that Wise can use for data-driven decisions
• Identify patterns, trends, or anomalies that can be optimised or improved 

• Summarise the main findings and recommendations in a clear format
• Prioritise recommendations based on impact, ease of implementation, and relevance to Wise’s business context

• If coding is required, ensure your code is clean, well-commented, and well-structured for readability
• Review your findings, structure, and visuals for clarity and accuracy

This structured guide should help you create a high-quality, focused, and efficient case study for Wise. 

Good luck!