Arturas joined the Office for National Statistics (ONS) as an economist after finishing his bachelors in Economics and has worked in several areas across the organisation. Throughout his career he built data processing pipelines, fostered the development of python programming and data science capability, worked in Data Science roles both in the wider ONS and within the Data Science Campus.
He has always had a passion for programming that went hand in hand with his interest in data. He is currently part of the scale-up team, working to productionise existing and future Campus work to make sure it is accessible and usable by a wide range of users.
This article describes our work with the National Institute for Health and Care Excellence (NICE), using Natural Language Processing to identify documents and recommendations that are relevant to guidance that they are reviewing.
Read more on Supporting healthcare guidance recommendations using natural language processing
This article summarises further development we have carried out to improve the timeliness and relevance of the shipping indicators, in response to feedback from our users.
Read more on Faster indicators of UK economic activity: more timely and relevant shipping indicators
Many datasets contain variables that consist of short free-text descriptions of items or products. This technical report describes how we developed and implemented a natural language processing (NLP) pipeline that produces a well structured and hierarchical dataset.
Read more on Optimus – A natural language processing pipeline for turning free-text lists into hierarchical datasets
