Product Analyst - Platform

Wise
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Studentjob.co.uk

Product Data Scientist

Data Analyst

Data Engineering Manager (Data Platform)

Product Data Analyst

Associate Product Data Analyst (FTC)

Company Description

Wise is on a mission to facilitate borderless transactions – instant, transparent, and eventually free. Our goal is to develop the first genuine multi-currency banking infrastructure, leveraging advanced technology solutions.

Job Description

The Platform Engineering Tribe:

We are the driving force behind Wise, making sure everything runs smoothly. We support our engineering teams so they innovate, build and ship great code quickly and confidently. Our expertise spans Data, Machine Learning, Incident and Risk Management, Hardware, and System Efficiency, all designed to foster an unparalleled Engineering Experience.

The Analytics Role:

As our second hire in this exciting space, you'll help shape the way we support our engineering teams. This role isn’t about finding technical solutions – we have awesome engineers for that. You will help refine our strategies, focusing on understanding user needs, mastering our data infrastructure, and leveraging metrics for impactful outcomes. Your influence will profoundly affect the trajectory and success of Wise’s mission.

About You:

Technical Skills:

Proficient in SQL and data visualization tools such as Looker, Superset, PowerBI, or Tableau.

Experienced in building and managing data pipelines using tools like DBT and Airflow.

Proficient in Python for data science, automation, or system integrations.

Soft Skills:

Curiosity:A desire to learn, an openness to new information, and the agility to revise opinions based on new evidence.

Strategic Influence:Exceptional capacity to build trust and influence across multidisciplinary teams without relying on authority, you can convey complex concepts clearly and engagingly to influence diverse audiences.

Adaptive Mindset:A commitment to ‘strong opinions, weakly held’—actively seeking out challenging perspectives and finding middle ground.

Why Should You Join Our Team?

At Wise, every day brings something new and exciting:

Diverse Creative Domains:You'll never be bored. With so many areas to explore, there's always something intriguing to dive you're a creative person, you'll love the opportunity to shape the domain and influence how we approach various challenges.

Full-Spectrum Growth:This role is perfect if you've already discovered your interests as a junior and are ready to push through in those areas while still experimenting with different aspects of the analytics function. You’ll get to experience a full stack of skills and a full spectrum of domains.

Balanced Independence and Support:Don’t worry—it’s not all on your shoulders. While you'll have the freedom to be fully independent where you want, you’ll also have access to a full team of cross-domain experts to support you when you need it

What will you be working on?

Behavioural Economics → Redefine Productivity -Investigate when engineers are most productive and create metrics that integrate system performance with developer satisfaction and work-life balance.

Data Science → LLMs for Incident Alerting:Leverage Large Language Models to analyse our thousands of post-mortems, refining our incident alerting systems with advanced natural language processing.

Data Engineering → Revolutionize ETL and KPI Tracking:Move beyond basic ETL processes by working with REST APIs, Airflow, and DBT to build efficient data pipelines and transform data. Develop and refine systems to automate and optimize key KPIs related to IT costs, providing actionable insights.

Your work will directly influence Wise’s success and trajectory. If you’re excited about advancing the boundaries of data and technology, apply now and be part of our future!

Benefits:

Competitive stock options in a profitable company.

Flexible working conditions tailored to support a balance between work and personal life.

Annual budget for personal and professional development.

Visa and relocation support for exceptional international candidates.

Commitment to Diversity:

Wise is dedicated to fostering a diverse and inclusive workplace. We strongly encourage applications from all backgrounds and are committed to providing equal opportunity in our employment practices.

If you are passionate about using your analytical skills to influence and improve IT operations within a leading international financial services company, apply now to join us at Wise!

Good Reads

State of our CI/CD pipeline () / ()

Additional Information

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.
Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit .

Keep up to date with life at Wise by following us on and .

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.