AI at CUBE
CUBE uses AI, Computer Vision 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 regulatory intelligence.
CUBE RegTransform is an AI-powered service that is completely unique to CUBE. RegTransform powers CUBE technologies and is the critical component behind CUBE’s interface that enables effective and accurate regulatory data management.
RegTransform is the technological magic that transforms regulatory data into regulatory intelligence at a scale and quality not possible at a human level.
As a Senior Data Scientist, you will be part of the RegTransform team, where you will develop and deliver core features of CUBE’s award-winning Regulatory Change Management platform used by the world’s largest Global Financial Institutions.
- Build, maintain, and proactively improve end-to-end scalable ComputerVision-based pipelines and machine learning services that will be integrated with CUBE’s customer-facing production applications.
- Design and develop leading solutions for Information Extraction and Data Enrichment including Computer Vision downstream tasks such as Object detection, Machine Translation, Document Classification, OCR, etc.
- Develop relevant customer insights & recommendations through executing and leading relevant analytical and visualization techniques.
- Self-manage and contribute to R&D and productionization activities for both internal and joint development projects.
- BSc/BE/B.Tech degree in Computer Science, Machine Learning, Data Science, Computational Linguistics, or relevant math-heavy quantitative fields.
- 3+ years (OR MSc and 2+ years) of hands-on experience with Computer Vision-focused machine learning and deep learning techniques.
- Proven experience in Machine Learning Operation (MLOps) and productionization of ML systems.
- Strong programming skills (in Python) and hands-on experience with deep learning frameworks such as PyTorch and TensorFlow.
- Experience in training models in GPU computing using NVIDIA CUDA or on the cloud.
- Experience in industry-standard scikit-image, PIL, OpenCV, matplotlib, seaborn packages
- Strong product development/deployment mindset.
- An ability to get workable solutions to MVPs quickly, and develop further automation, intelligence and learning iteratively.
- Strong problem-solving skills with an innovative mindset and diligent “can do” and ”get it done” attitude.
- Self-motivation with excellent time management and verbal/written communication skills.
- MSc in Computer Vision, Computational Linguistics, Machine Learning, Data Science, Computer Science, or relevant quantitative fields.
- In-depth knowledge of modern Computer Vision techniques for general and down-stream tasks (e.g., OCR, Image processing, Relation Extraction, object detection and classification) and practical experience in developing/utilizing SOTA models such as Tesseract, Detectron2, YOLO and Layout Parser.
- Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
- Proven experience in building and deploying ML products within cloud environments.
- Track record of dealing well with ambiguity, prioritising needs, and delivering results in a dynamic environment.
- Experience managing both technical and non-technical stakeholders and resolving complex business and technical issues.
Why you'll love CUBE?
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.
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.
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.
Cutting-edge Regulation Transformation Engine: RegTransform is the technological magic that transforms regulatory data into regulatory intelligence at a scale and quality not possible at a human level. To enable this transformation, our data science team is actively developing and deploying state-of-the-art Computer Vision, NLP and ML models to identify relevant categories and extract key information (entities, relationships, ...) from the unstructured data and generate high quality structured data.
Always learning: Part of your job is to stay up-to-date with the latest research, and share your learning with the 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.
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.
Extremely bespoke hiring process: At CUBE, we're trying to flip hiring on its head: the objective of the process is to create a personalized 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.
More About CUBE
CUBE was founded in 2011 to transform the way global financial institutions manage regulatory change. Few financial institutions have instant access to the regulatory intelligence and analytics required to understand the impact of regulatory change, and tackle it, quickly and cost-effectively.
Utilizing Artificial Intelligence, Machine Learning and Natural Language Processing, CUBE’s enterprise-wide RegTech solution de-risks the regulatory change process and dramatically cuts compliance costs. CUBE is a fast-growing business, with offices in the UK, USA, and Australia. We serve multi-jurisdictional Tier 1/2 financial institutions, including global banks, wealth managers and insurance companies. 1.5-million staff in 180 countries consume regulatory intelligence and manage regulatory change initiatives, powered by CUBE.
⏱️ 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 5 and 10 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 info flow in both directions.
Here's what will happen:
- Online application (link below)
- First round video interview with RegTransform's Lead Data Scientist and one of the senior data scientists (45-60m)
- Take-home challenge
- Second round video interview with our data science team (45-60m)
- Final round panel interview, again over video (45-60m)
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!