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- Tasks: Drive data-driven decisions to combat financial crime and enhance customer onboarding.
- Company: Join Wise, a mission-driven company making money transfers easy and affordable worldwide.
- Benefits: Enjoy a competitive salary, RSUs, and a diverse, inclusive workplace culture.
- Why this job: Make a real impact by collaborating with teams to improve financial security for customers.
- Qualifications: Strong quantitative skills, SQL/Python experience, and 3-4 years in data analysis preferred.
- Other info: Work in a dynamic team of over 100 analysts and develop your technical skills.
The predicted salary is between 60000 - 75000 £ per year.
Company Description
At Wise, our mission is Money Without Borders - instant, convenient, transparent, and eventually free. Whether our customers are sending money to another country, spending money abroad, or making and receiving international business payments, Wise is committed to making their lives easier and saving them money.
Job Description
We’re looking for a Product Analyst passionate about our mission to partner with our financial crime product teams. Your role will involve driving data-driven growth decisions and helping to combat financial crime. As a Product Analyst, you'll lead analytics efforts within the Financial Crime product team, balancing fraud prevention with a smooth customer onboarding experience. You’ll collaborate with a team of over 100 Analysts, work on cross-team projects, develop technical skills, and propose improvements for analytics at Wise. Most importantly, you'll work closely with product managers, designers, and engineers to translate insights into real change for our customers and support our mission.
Your mission:
- Contribute to the Financial Crime team by making data accessible and actionable for Wise’s product teams.
- Create and track key metrics.
- Support stakeholders with research and visualizations.
- Collaborate with data scientists on fraud detection features.
- Help develop risk management frameworks.
The role involves collaboration with Product, Data Science, and Operations teams.
Qualifications
We look for candidates with strong quantitative skills, experience with SQL and Python/R, excellent communication skills, and the ability to independently structure and prioritize business problems. Experience with data visualization tools and 3-4 years of professional data analysis is preferred. Additional skills such as experience in fincrime, machine learning, or A/B testing are a plus.
Additional Information
Base salary: 60,000 - 75,000 GBP gross annually, based on experience and interview outcomes. We offer Restricted Stock Units (RSUs) and other benefits. Wise is committed to diversity, equity, and inclusion, fostering a respectful and empowering environment for all employees. To learn more about working at Wise, visit our website and follow us on social media.
Senior Product Analyst - FinCrime employer: Wise
Contact Detail:
Wise Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Product Analyst - FinCrime
✨Tip Number 1
Familiarise yourself with Wise's mission and values. Understanding their commitment to making money transfers easier and more transparent will help you align your answers during interviews and demonstrate your passion for their goals.
✨Tip Number 2
Brush up on your SQL and Python/R skills, as these are crucial for the role. Consider working on personal projects or contributing to open-source projects that involve data analysis to showcase your technical abilities.
✨Tip Number 3
Network with current or former employees of Wise, especially those in similar roles. They can provide valuable insights into the company culture and the specific challenges faced by the Financial Crime product team.
✨Tip Number 4
Prepare to discuss your experience with data visualisation tools and how you've used them to drive decisions in past roles. Be ready to share specific examples of how your analyses have led to actionable insights.
We think you need these skills to ace Senior Product Analyst - FinCrime
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and qualifications for the Senior Product Analyst position at Wise. Tailor your application to highlight how your skills in data analysis, SQL, and Python/R align with their needs.
Craft a Tailored CV: Your CV should reflect your experience in financial crime analysis and data visualisation. Emphasise relevant projects or roles that demonstrate your quantitative skills and ability to drive data-driven decisions.
Write a Compelling Cover Letter: In your cover letter, express your passion for Wise's mission of 'Money Without Borders'. Discuss how your background in data analysis can contribute to combating financial crime and improving customer experiences.
Highlight Collaboration Skills: Since the role involves working closely with product managers, designers, and engineers, be sure to mention any past experiences where you successfully collaborated across teams. This will show your ability to work in a multidisciplinary environment.
How to prepare for a job interview at Wise
✨Understand the Company Mission
Before your interview, make sure you fully grasp Wise's mission of 'Money Without Borders'. Be prepared to discuss how your skills and experiences align with this mission, especially in relation to combating financial crime.
✨Showcase Your Technical Skills
As a Senior Product Analyst, you'll need strong quantitative skills. Be ready to demonstrate your proficiency in SQL and Python/R during the interview. Consider preparing examples of past projects where you've used these tools effectively.
✨Prepare for Collaboration Questions
Since the role involves working closely with product managers, designers, and engineers, expect questions about teamwork and collaboration. Think of specific instances where you've successfully worked in cross-functional teams and how you contributed to achieving common goals.
✨Highlight Your Problem-Solving Abilities
The ability to independently structure and prioritise business problems is crucial. Prepare to discuss how you've approached complex data analysis challenges in the past, including any frameworks or methodologies you used to arrive at solutions.
