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 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 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.
- Design new ML/DL/NLP models and algorithms and develop PoCs and MVPs.
- Write production-level code and communicate with the entire technical team to impact algorithm production.
- Design, develop, and deploy automated scalable data science pipelines and machine learning services that will be integrated with CUBE’s customer-facing production applications.
- Deliver leading solutions for Data Enrichment, Information Extraction (e.g., Named Entity Recognition, Relation Extraction) and Text Mining, Ontology / Semantic Linking, Content Classification, Deep Learning, Predictive Modelling, etc.
- Direct contributions to projects implementing/applying advanced NLP methods (e.g., language modelling, transfer learning, self-attention mechanism, transformer architectures, and use of state-of-the-art models such as GPT, BERT, XLNet, etc.).
- Contribute to the business’s AI and analytics engine by bringing additional capabilities that anticipate demand from the wider market for Data/ML solutions.
- Take leadership in kick-starting, productionizing, and continuously improving the platform’s existing, proposed, and future capabilities.
- Develop relevant customer insights & recommendations through executing and leading relevant analytical and visualization techniques.
- Self-manage and contribute to R&D and productionization activities (within on-prem and/or cloud environments) - including both internal projects and joint development projects.
- Manage requirements and scope while delighting customers/stakeholders on results.
- BSc/MSc degree in Computer Science, Machine Learning, Data Science, Computational Linguistics, or relevant quantitative fields.
- 7+ years of experience as a data scientist with excellent knowledge of current technologies and techniques to deliver leading AI/ML solutions to real business problems
- Demonstrated experience in implementing on-prem and cloud-based end-to-end AI/ML systems, including data processing, feature engineering/transformation, and tuning of ML models in training and production (MLOps) - with both structured and unstructured data.
- An ability to get workable solutions to MVPs quickly, and develop further automation, intelligence and learning iteratively.
- Proven experience in building and deploying ML products within cloud environments.
- Profound knowledge of the key analytical techniques and machine learning and deep Learning methods.
- In-depth knowledge of modern NLP techniques for general and down-stream tasks (e.g., Language Modelling, Machine Translation, Named Entity Recognition, Relation Extraction, Summarization, and Dialog Systems) and practical experience in developing/utilizing SOTA models such as GPT, BERT, and XLNet.
- Hands-on experience with deep learning frameworks such as PyTorch, TensorFlow and Keras.
- Experience in managing big data and establishing/managing billions of relationships between data attributes.
- Strong programming skills, including scripting languages for data processing and development (e.g., Python, R, or Scala), and data-querying skills (SQL, Spark, etc.).
- Proven experience with development, integration, and deployment tools and cloud environments (e.g., Azure, AWS, GCP)
- Very strong product development/deployment mindset.
- 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.
- Proactiveness in raising issues and improving models, pipelines, and processes.
- PhD in Computer Science, Machine Learning, Data Science, Computational Linguistics, or relevant quantitative fields.
- Publications in top-tier ML/NLP/DL journals and conferences such as ACL, EMNLP, COLING, KDD, ICML, NeurIPS, or similar
- 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.
- Track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment
- Experience managing both technical and non-technical stakeholders and resolving complex business and technical issues.
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.
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 NLP and ML models to 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.
⏱️ 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 (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!