🤖 AI at CUBE
CUBE uses AI and NLP to machine read the regulatory internet, at global scale. We collect, clean, standardise, translate, monitor, classify, and enrich regulatory data across 180 countries in over 60 languages. All in near real-time.
We've even built our own ontology of regulation—machine-driven and continuously refined by a team of subject matter experts.
On a high level, CUBE uses AI to transform regulatory data into customer-specific regulatory intelligence. And this is exactly where RegBrain comes in.
It's always a great time to become a CUBER, but now literally could not be a better time. This year, we are expanding the core RegBrain team, having established our data and infrastructure foundations within GCP. You will not only lead the development from these foundations, but also play a critical role in the expansion of the team (we are aiming to double by the end of 2022).
CUBE has the world's most comprehensive corpus of regulatory content, and it has been collected, cleaned, standardised, translated, and classified by the RegTransform AI teams. The RegBrain team works with not only this structured (and enriched!) regulatory text, but also customer data from our core platform and 3rd party partners. No other team at CUBE has ever had ownership over all of this data.
🚀 The mission: to take CUBE's AI to the next level by a) creating the ultimate semantic map of global regulation and b) learning from the intersection of customer and regulatory data.
The RegBrain team is responsible for the end-to-end research, design, and development of both the semantic map and a suite of AI-driven capabilities—including recommendation systems, regulatory change prediction, and sentiment analysis.
🏗️ Core responsibilities
As the lead data scientist / machine learning engineer, your main responsibility is to oversee the development and productionisation of ML and NLP prototypes. Your end products are APIs that can be consumed by CUBE's core platform or incorporated into CUBE's own API, RegConnect.
- Determine the cloud architecture strategy and overall ML systems for RegBrain, guided by MLOps principles. All of our models must be scalable, monitored, and continuously improved.
- Lead—and expand—a team of data scientists and machine learning engineers. Mentorship and management are equally important.
- Guide the selection and development of optimal ML & NLP models for each RegBrain use case, leveraging SOTA approaches wherever appropriate (but also recognising when a simpler model is preferable).
- Improve the efficiency, performance, and scalability of ML & NLP models (this includes data quality, ingestion, loading, cleaning, and processing).
- Oversee the development and refinement of the semantic map (knowledge graph) of CUBE's regulatory data.
- Provide input for the RegBrain roadmap, working closely with the Head of Product.
- Provide input for data pipeline requirements, working closely with our data engineering team.
- Stay up-to-date with ML & NLP research, and experiment with new models and techniques.
💪 Core requirements
- Extensive experience with end-to-end model design and deployment within cloud environments (bonus points for GCP) ☁️
- Extensive experience with ML & DL platforms, frameworks, and libraries 📚
- Experience with and passion for MLOps best practises 🌀
- Experience analysing vast volumes of textual data (almost all of our use cases will involve NLP) 🔠
- Strong familiarity with SQL and NoSQL/graph databases (bonus points for ArangoDB) 🏦
- Solid understanding of data structures, data modelling, and software architecture 🏛️
- Team management and mentorship experience 👩🏽🏫
- Ability to write clear, robust, and testable code, especially in Python 🐍
- Strong grasp of data visualisation techniques (for dashboarding, reporting, etc.) 📊
- A systems thinking approach 🌐
- A mathematically and statistically-oriented brain 🔢
- A healthy sense of humour (you're going to need it... don't say we didn't warn you 😉)
Experience matters. But what is more important than raw number of years of experience is demonstrated proficiency (through GitHub profiles/online portfolios and the interview process itself). Bonus points for Stack Overflow and Kaggle contributions! 💯
💝 Why you'll love RegBrain (& CUBE)
If there is a best time to join RegBrain, it's now. Here are the many reasons why.
🌍 Immediate global impact. CUBE is a well-established player in 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). We have an audience across 150 countries, and they love CUBE.
🗽 Freedom & flexibility. Think of RegBrain as a fully-funded startup within a scaleup. Over the past six months, we've only laid the basic foundations within GCP: you will have a significant influence over how we develop our tech stack and ML ways of working. To Airflow or not to Airflow? PyTorch or TensorFlow? You decide. As long as you can justify your choices, the rings of Saturn are the limit.
📊 Quantity & quality of data. The stage has literally been set: over the past 10 years, the five engineering teams at CUBE have built solid foundations for data collection, transformation, and classification. The RegBrain team focuses solely on learning from this mountain of structure.
🗣️ A rich & complex dataset. The main dataset is not only already structured, but also longitudinal and multilingual. We've tracked changes to regulation over time and built in-house translation models for 60+ languages.
📚 Always learning. Part of your job is to stay up-to-date with the latest research, and share your learning with the RegBrain team and other AI teams at CUBE. You'll have a training budget and a conference budget. In the mid-long term, we're aiming to collaborate with universities.
⚖️ Responsible AI. We will proactively address the inevitable biases that emerge for any AI system. Our Head of Product was trained at the Oxford Internet Institute and has direct connections with ethicists who are influencing the future of AI regulation.
💻 Employee-first work-life policy. CUBE went fully remote before the pandemic even hit, because we wanted to define the future of work. As a CUBER, you'll be able to design your home office and choose your own work equipment. Unable to work from home one week, or desperate for in-person interaction with colleagues? No problem—book a room in a coworking space.
🌱 Sustainable, customer-driven growth. We are a bootstrapped company funded by customers and strategic private investment. This means that growth is sustainable, and product development is very closely aligned with customer needs.
🌎 Visa sponsorship if required. We know every single nuance of Skilled Worker visas.
🦄 Extremely bespoke hiring process. At CUBE, we're trying to flip hiring on its head: the objective of the process is to create a personalised job description (and title). This page sets the general context. We'll collaboratively determine the best role for you, given your interests, CUBE's needs, and other members of the team.
⏱️ Hiring timeline
We know how insufferably long and complicated hiring processes can be. We've been there before.
That's why at CUBE, we aim to compress the hiring timeline to between 10 and 15 days (from the first-round interview to the final round). There's no HR screen, culture fit interview, or coding on a whiteboard. Just high-quality infoflow in both directions. 🌊
Here's what will happen:
- Online application (link below 👇)
- First round video interview with RegAI's Head of Product (30-45m)
- Second round video interview with the CTO of RegPlatform (30-45m)
- Take-home challenge (it'll be fun, we promise, and we won't ask for more than a few hours of your time)
- Final round panel interview, again over video, with product, data science, and engineering representatives (60-90m)
If you have any questions at this stage, feel free to use the live chat widget on this page. Otherwise: what are you waiting for? This is your once-in-a-lifetime opportunity to define the future of regulation. The clock is already ticking. 🕰️