Graduate Analyst

Wise•Austin, TX

About The Position

Wise is hiring three graduates to work as analysts within its North America product teams, which build products for personal and business customers in the US and Canada. The full-time role begins in September 2027. Graduates join the global Graduate cohort, are onboarded at one of Wise’s offices, and receive mentorship and work reviews from a senior analyst while collaborating with product managers, engineers, and analysts across North America. Analysts join one of four teams: Onboarding, Send, Account, or Growth. The role focuses on product discovery and helping teams make decisions about what to build and whether it worked. Analysts investigate customer behavior and feedback, assess opportunities, help prioritize product work, and, as they develop, lead discoveries and present recommendations. They help design and analyze A/B tests and pre/post comparisons, monitor core metrics, and communicate findings through writing, charts, and presentations. Analysts also build and maintain data models and dashboards using Snowflake, dbt, Git, Airflow, Python, and Lightdash, and help improve AI skills used for routine reporting. Wise uses AI tools daily; analysts are responsible for checking data, logic, and conclusions before decisions are made. By three months, the graduate is expected to own part of the team’s regular metrics and deliver an initial analysis. By six months, they are expected to lead a discovery or experiment readout from question to recommendation and present it to their team. Team areas include onboarding and verification; payment methods, payouts, and payment risk; Wise accounts, debit cards, and balances; and customer acquisition, promotions, corridor strategy, and pricing.

Requirements

Bachelor’s or master’s degree in a quantitative field such as economics, statistics, mathematics, computer science, or engineering, completed by the start date, or equivalent practical experience.
Experience using SQL, Python, or R to answer a real question with data, through coursework, a project, an internship, or independent work.
Understanding of statistical basics, including distributions, hypothesis tests, and confidence intervals, and ability to distinguish meaningful differences from noise.
Curiosity about customer behavior and ability to consider the product implications of analytical results.
Ability to explain findings to non-technical audiences in writing and conversation, and comfort acknowledging when an answer is not yet known.
Ability to follow through, ask for help early, and act on feedback.
Use of AI tools to work efficiently and judgment to check their output before relying on it.
Authorization to work in the US; visa sponsorship is not available.

Nice To Haves

Hands-on experience with Snowflake, dbt, Git, Airflow, or Lightdash.
Exposure to A/B testing or causal inference through coursework or practice.
An internship or project in analytics, data science, product, or finance.
Interest in payments, fintech, or how people move and manage money across borders.

Responsibilities

Investigate customer funnels, behavior, feedback, and survey responses to identify customer difficulties and opportunities.
Assess and size opportunities to help product teams prioritize work with the greatest impact.
As experience grows, lead product discoveries and present recommendations to product managers.
Help design and analyze A/B tests and pre/post comparisons for new features, and recommend whether to roll out, iterate, or roll back changes.
Monitor core team metrics and flag potential issues.
Build and maintain data models and dashboards using the team’s data tools, and help improve AI skills used for routine reporting.
Communicate findings in writing, charts, and presentations to the team and wider North America squad.
Validate AI-generated output by checking the data, logic, and conclusions before decisions are made.
Within the first three months, own part of the team’s regular metrics and deliver an initial analysis.
Within the first six months, lead a discovery or experiment readout from question to recommendation and present it to the team.