AI Product Manager Product - AI · London ·

CUBE Content Governance Global Limited
London
2 months ago
Applications closed

Related Jobs

View all jobs

AI Product Manager – Legal only

Senior Product Manager - AI, ML & Data Science

Senior Machine Learning Product Manager (Deploy)

Product Manager

Machine Learning Manager, London

Data Science Manager

Role:AI Product Manager

Location:London, UK (1 day per week in the London office is required)

Recently listed as a "RegTech Top Performer" in Market Fintech's RegTech Supplier Performance Report, CUBE is pioneering the development of machine automated compliance.

We are a global RegTech business defining and implementing the gold standard of regulatory intelligence and change for the financial services industry. We deliver our services through a SaaS platform, powered by an innovative combination of AI and proprietary data ontology, to simplify the complex and everchanging world of compliance for our clients.

At CUBE, we are creating the future and are a company rooted in strong values, team spirit and commitment to our customers and wider communities. We serve some of the largest financial institutions globally and are expanding our footprint very fast. As we do so, we are keen for new talent to join us and realize their full potential to grow into leadership positions within the business.

Role mission:

The AI product team is situated at the intersection of the machine learning and regulatory subject matter expert teams. As AI Product Manager, you ensure that our AI product suite delivers output of sterling quality and provides maximum value to our users, by driving the creation of expert and user-in-the-loop feedback systems.

You will work across at least one of these three areas and drive the end-to-end product development process, from discovery through to testing and delivery. You will also influence the roadmap for improvements, working closely with the Head of Product for AI.

Responsibilities:

  1. Manage the end-to-end discovery, testing, and delivery process for AI products, working closely with our machine learning, regulatory subject matter expert, customer, and software engineering teams.
  2. Oversee model training, validation, and testing processes, by establishing rigorous reviewer guidelines, recruiting the appropriate domain experts, and determining sampling strategy/size.
  3. Deeply understand the pain points felt by our compliance users, especially as they relate to the output of AI products.
  4. Monitor the performance of our AI products, and use the results to propose and drive forward enhancements.
  5. Define and review the production of AI product marketing collateral to support the go-to-market teams.
  6. Reduce information silos between our machine learning and regulatory subject matter expert teams.
  7. Provide input for the global AI roadmap, working closely with the Head of Product for AI.
  8. Stay up-to-date with both regulatory and AI trends.

What we’re looking for:

  1. Experience with building AI SaaS products from ideation to launch, especially those involving NLP and NLU.
  2. Proficiency in Python for data analysis, Excel, and SQL (fluency in data).
  3. Hands-on experience in data-science or ML engineering environments.
  4. Understanding of API design.
  5. Ability to collaborate with multiple stakeholders and cross-functional teams (data science/machine learning, engineering, subject matter experts, customer services, sales, marketing).
  6. Proficiency in diagram creation and visualisations (especially important in a remote-first organisation).
  7. Experience with Confluence and Jira.
  8. Attention to detail without compromising on the big picture.

Why Us?

Globally, we are one of a kind!

CUBE are a well-established market leader within Regtech (we were around before Regtech was even a thing!), and our category-defining product is used by leading financial institutions around the world (including Revolut, Citi, and HSBC).

Growth & progression

Last year we grew by more than 50% and our growth journey is just getting started! We are a dynamic, fast-paced workforce that is always seeking ways to accelerate our people, processes, services and products. We hire ambitious people that want to make a difference, share their ideas, “make it happen” and find better, smarter ways of working. Our future is shaped by our employees, so if you’re someone looking for an opportunity to make a real impact, and progress your career alongside the business, it couldn’t be a better time to join us!

Internationally collaborative culture

With more than 650 CUBERs across 17 locations in EMEA, the Americas and APAC, collaboration is key to our success. We are a diverse workforce united by a shared desire to reshape the world of regulatory compliance and make an impact. We champion sharing knowledge with colleagues from all over the world, in order to deliver the best results.

Innovative breakthrough technology

CUBE is an innovator. We pioneered the use of AI in the field of regulatory change and our state-of-the-art, cutting edge technology is helping financial services firms from all over the world, solve complex compliance challenges. You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that will reshape the world of regulatory compliance.

Work life balance

CUBE is a remote-first business so you will be able to design your home office and choose your own work equipment. We host regular in-person meet-ups as a chance to get-together, share ideas and collaborate with other teams but we are advocates for remote working and we believe working remotely provides freedom to innovate, create and unlock global talent.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.