Graduate Analyst

About the Role

Wise is a global technology company building the best way to move and manage the world's money. As a Graduate Product Analyst, you will join the North America Regional Analytics team in September 2027. This full-time role offers an opportunity to make an impact early in your career. You will be onboarded with the global Graduate cohort and learn the basics of being a Wiser. You will work as an analyst within North America product teams, supporting product managers and engineers in deciding what to build next and evaluating its success.

You will join one of four teams: Onboarding, Send, Account, or Growth, each focusing on a different stage of the customer journey. A senior analyst will mentor you, review your work, and help you grow, and you will be part of a team of analysts covering the entire North America product.

Key Responsibilities

• Shape Strategy & Prioritization: Help your team understand customer struggles and prioritize fixes. This involves analyzing funnels and customer behavior, reviewing customer feedback and survey responses, and sizing opportunities to ensure the team prioritizes work with the biggest impact. As you grow, you will lead your own discoveries and present recommendations to your product manager.
• Learn Launch Rigor & Experimentation: Assist in designing and analyzing A/B tests and pre/post comparisons for new features, and translate results into clear recommendations (roll out, iterate, or roll back). Monitor your team's core metrics and flag anomalies.
• Build on the Semantic Layer: Learn to build and maintain data models and dashboards to enable your team to answer their own questions. You will use Snowflake, dbt, Git, Airflow, Python, and Lightdash, and help improve AI skills for routine reporting.
• Share What You Learn: Communicate your findings to your team and the wider North America squad through written reports, charts, and in-person discussions, ensuring your work leads to decisions.

AI with Judgment

You will use AI tools daily, focusing on owning the answer rather than the tool. This includes checking data, logic, and conclusions before any action is taken.

Teams

• Onboarding: Focuses on the initial customer journey, including profile creation, verification, and first actions. Helps genuine customers proceed quickly while preventing fraudulent actors.
• Potential questions: Where do new customers drop out between signing up and their first transfer, and what would bring them back? Which verification steps slow genuine customers down without catching more fraud or financial crime?
• Send: Covers how customers send money, with sub-teams for:
• Pay-in: How customers pay money into Wise (e.g., bank transfer, card).
• Payout: How money reaches recipients' bank accounts, balancing cost, speed, and conversion.
• Payment Risk: Manages transfer suspensions and chargebacks.
• Potential questions: Which payment methods help customers complete their transfer, and what does each one cost us? What would make these payouts faster or cheaper, and how much more volume would that bring? How do we cut losses from chargebacks and fraud without blocking good customers?
• Account: Focuses on customers using Wise as an account for receiving payments (USD and CAD), spending with the Wise debit card, and earning returns on held money.
• Potential questions: What makes a customer start getting paid into their Wise account, and what makes them keep doing it? Who uses the Wise card, for what, and what stops others from using it? Does holding money with Wise change how customers send money abroad?
• Growth: Covers the top of the funnel, including how people find Wise and decide to try it. This involves landing pages, promotion engines, corridor strategy, and pricing.
• Potential questions: Did a promotion bring in customers who stay, or customers who leave once it ends? Which corridors should we focus on next, and why?

Your First Six Months

• By three months: You will own part of your team's regular metrics and have delivered your first analysis.
• By six months: You will have led a discovery or an experiment readout from question to recommendation and presented it to your team.

Requirements & Tech Stack

• SQL, Python or R for data analysis
• Basic understanding of statistics (distributions, hypothesis tests, confidence intervals)
• Snowflake, dbt, Git, Airflow, Python, Lightdash (hands-on experience is a plus)
• A/B testing or causal inference exposure (a plus)
• AI tools

Qualifications

• A bachelor’s or master’s degree in a quantitative field (e.g., economics, statistics, mathematics, computer science, engineering), or equivalent practical experience.
• Demonstrated experience using SQL, Python, or R to answer questions with data (through coursework, projects, internships, or personal work).
• Understanding of statistical basics and the ability to differentiate real differences from noise.
• Curiosity and a strong product sense, with a desire to understand customer behavior and its implications.
• Clear communication skills, with the ability to explain findings to non-technical audiences.
• Ownership: Completing tasks, seeking help early, and acting on feedback.
• Authorized to work in the US. Visa sponsorship is not offered.

Additional Information (Nice to Have)

• Hands-on experience with Snowflake, dbt, Git, Airflow, or Lightdash.
• Exposure to A/B testing or causal inference.
• An internship or project in analytics, data science, product, or finance.
• An interest in payments, fintech, or cross-border money movement.