NLP Data Scientist / Machine Learning Engineer

About CUBE

CUBE is a powerful RegTech solution that delivers unprecedented cuts in regulatory costs, and minimizes your risk of non-compliance - whichever jurisdictions, states or countries you operate in.

We automate the capture of global regulatory data, alert you to regulatory changes that pose a compliance risk to your business, pinpoint all policies, procedures and controls that are affected, and enable rapid remediation. We utilize Artificial Intelligence and Machine Learning to achieve the smartest and most efficient automation possible.

Over many years CUBE has built deep insight into the impact of regulatory change on financial services institutions, and we are uniquely positioned to ensure that you respond to global regulatory change in the most compliant way possible.

Position

Research & data science
Core mission: Design ML & NLP prototypes for each RegBrain use case, and own the semantic map of CUBE's regulatory data.

• Prepare, maintain, and refine the semantic map (knowledge graph) of CUBE's regulatory data.
• Develop, test, and improve optimal ML & NLP models for each RegBrain use case.
• Present information using data visualisation techniques (especially important for the semantic map).
• Determine additional data sources and how to include them in the pipeline (another team will help with actually adding them).
• Stay up-to-date with ML & NLP research, and experiment with new models and techniques.

ML & data engineering
Core mission: Develop the ML & NLP prototypes from the data science team, resulting in APIs that can be consumed by CUBE's core platform.

• Determine the cloud architecture strategy and overall ML & data systems for RegBrain.
• Work closely with other AI engineering and data teams to ingest data from our core platform, our transformation engine, and other sources.
• Improve the efficiency, performance, and scalability of ML & NLP models (this includes data quality, ingestion, loading, cleaning, and processing).
• Improve the efficiency, performance, and scalability of the semantic map.
• Verify that the quality of results in production meets the requirements.

Your qualification

Just as the responsibilities of the RegBrain subteams overlap, the core competencies we're looking for overlap too. The good news for you is that we will use your preferences and the interview process to collaboratively determine which side of the spectrum you should sit on. The strongest candidates have competencies across both sides (and are as modular as CUBE's core product!).

• End-to-end ML model design and development experience (design is more relevant for the data science team; deploying models to production and performance monitoring are especially important for the engineering team) 
• Experience with cloud infrastructure for data pipelining and model deployment (more relevant for engineering) ☁️
• Experience with ML platforms, frameworks, and libraries 
• Experience analysing vast volumes of textual data 
• Strong familiarity with SQL and NoSQL/graph databases 
• Solid understanding of data structures, data modelling, and software architecture 
• 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)

What we offer

• Visa sponsorship if required. We know every single nuance of Skilled Worker visas.