At a Glance
- Tasks: Own KPIs, analyse onboarding data, and drive product improvements.
- Company: Join Wise, a global tech leader in money management.
- Benefits: Enjoy stock options, generous leave, and a vibrant office culture.
- Other info: Fast-paced environment with opportunities for professional growth and a 6-week sabbatical.
- Why this job: Make a real impact on how businesses onboard with innovative data solutions.
- Qualifications: 5+ years in analytics, strong SQL skills, and experience with data-driven decision making.
The predicted salary is between 63000 - 77000 £ per year.
Wise is a global technology company building services for moving and managing money worldwide. Its products support international transfers, spending abroad, and international payments for individuals and businesses.
Responsibilities:
- Own the KPI tree and core metrics for business onboarding, ensure alignment, and communicate recommendations to senior stakeholders.
- Use data exploration and funnel analysis to identify high-impact opportunities in the onboarding flow.
- Partner with the team to prioritise high-leverage projects.
- Lead experimentation and A/B tests for business onboarding, define hypotheses and metrics, and analyze results.
- Drive product roadmap recommendations to reduce friction and improve conversion.
- Build and maintain data products and dashboards for self-service data literacy among PMs, design researchers, and engineers.
- Design LLM-ready data foundations for onboarding analytics, including business entities, event schemas, semantic definitions, metadata, retrieval-friendly documentation, and evaluation datasets.
- Partner with the product manager, engineers, and designers throughout the product development lifecycle to build and iterate product changes based on insights.
Requirements:
- 5+ years of experience in analytical roles.
- Excellent SQL skills and the ability to write clear, performant, reliable queries across large and complex datasets.
- Strong experience with at least three of funnel analysis, customer segmentation, cohort analysis, metrics decomposition, experimentation, causal inference, or product opportunity sizing.
- Experience building and owning production-grade analytical assets, such as dbt models, Airflow jobs, semantic layers, metric stores, governed dashboards, or trusted self-serve datasets.
- Understanding of data foundations required for AI systems, including clean entities, stable identifiers, clear definitions, metadata, access controls, provenance, quality tests, and retrievable documentation.
- Ability to work independently in a fast-paced environment and identify and deliver high-impact opportunities.
- Ability to prioritise requests by impact and choose between deep analysis and 80/20 delivery.
- Excellent verbal and written communication skills.
- Ability to create dashboards and visualisations with clear narratives and diagnostic power.
- Ability to structure problems before analyzing data and identify meaningful signals.
Nice to have:
- Prior B2B experience.
- A/B testing or causal inference experience.
- Familiarity with onboarding frameworks and KYC/KYB processes.
- Experience using AI and machine learning to improve analytical results and automate routine work.
Conditions:
- Company Restricted Stock Units.
- Numerous great benefits in the London office.
- Paid annual holiday, sick days, parental leave and other leave opportunities.
- 6 weeks of paid sabbatical after 4 years at Wise on top of annual leave.
product analyst business onboarding in London employer: Enfint
As a leading innovator in AI products for major publishers, our company offers an inspiring work environment where creativity and technology intersect. We prioritise employee growth through continuous learning opportunities and foster a collaborative culture that values diverse perspectives. Located in a vibrant city, we provide competitive salaries, relocation support, and the chance to make a real impact in the media landscape.
StudySmarter Expert Advice🤫
We think this is how you could land product analyst business onboarding in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Enfint!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like product analyst business onboarding at Enfint.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Enfint.
✨Apply Directly through Our Website
When you find a suitable opening like product analyst business onboarding at Enfint, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace product analyst business onboarding in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Enfint, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Enfint. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Enfint
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Enfint!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
