Technical Lead - (Business Classification Scheme)

Altran UK Holding Limited
£500 per day
12 Jul 2017
21 Jul 2017
As a global leader in Engineering and R&D services (ER&D), Altran offers its clients a new way to innovate by developing the products and services of tomorrow. Altran works alongside its clients on every link in the value chain of their project, from conception to industrialisation. For over thirty years, the Group has provided its expertise to key players in the Aerospace, Automotive, Defence, Energy, Finance, Life Sciences, Railway and Telecoms sectors, among others. In 2016, the Altran group generated revenues of €2.120bn. With a headcount of more than 30,000 employees, Altran is present in more than 20 countries. As Technical Lead with Altran, you will be fundamental to our ability to deliver to our customers and clients. Our customer expectations are high, as such we are looking for adaptive driven people to deliver on time to required quality. The project's objective is to provide a way of classifying all unstructured content so that users can find it and share it, on an enterprise basis. With growing amounts of data and information being generated, we must increase understanding of how it needs to use this information and how the information flows from one area/user to another. We want to proactively give users the information they need to do their job - a key enabler for that vision. Our aim is to support the Business Classification Scheme so that it's easier for users to navigate through large volumes of information. It should improve results from search engines, which can exploit both the improved metadata (to increase the accuracy of a search) and the scheme's structure (to narrow searches and organise results). The BCS element of the project team will also continue R&T investigations, and use government geared funding, into how to capture people's knowledge in a platform independent manner so it can be stored 'indefinitely' and allow it to be used/retrieved in lots of different ways. Responsibilities: - Working with the Capability Lead to create and roadmap - Working with the TeamCenter document management lead to integrate BCS understanding and capability into PLM TeamCenter - Work with follow members of the project team to understanding information and knowledge management capabilities and how the BCS strategy and capabilities can be integrated - Continue the novel research into platform independent knowledge modelling using government geared funding and external resource - seeking patentable opportunities where possible - Creating and leading spin off projects to develop and deploy self-contained capabilities as a result of platform independent knowledge modelling - Continue the terminology recognition capability development and seek opportunities to productionalise the capability and deploy - Developing an approach to capture business information and knowledge context to help support the project's programme - ability to give the users the information they need before they know they need it, due to in depth understanding of how information links to processes, users roles and daily tasks - Responsible for the creation and storage/management of engineering taxonomies, which are identified as part of the BCS. Where possible business owners are identified, but approach and management process will need to be defined with central IT organisation Qualifications/Experience: - Expected Skills/Knowledge includes; - o Taxonomy and ontologies - o Library sciences -i.e. classifying information - o Linking information together - o Project Management - building plans/schedules and coordinating projects and resource - Experience in the following to support the PIKM project includes; - o Programming Languages: Java, Javascript (including frameworks like jQuery and Angular - o Semantic Web Technologies: OWL, RDF, SPARQL - o Knowledge Representation: Protégé - o Natural Language Processing (NLP): Statistical methods approaches (including: TF-IDF, Latent Semantic Analysis, Distributional Analysis), Machine Learning approaches (HMM, CRF) - o Operating Systems: GNU/Linux and Windows - An awareness of the following; - o Programming Languages: Python and PHP - o Data Visualisation Packages: D3 (Data Driven Documents), Highcharts. - o Knowledge Representation: Apache Jena, TopQuadrant TopBraid Composer, NeOn toolkit - o Apache Lucene, Apache Solr, Microsoft SharePoint, Microsoft FAST, Apache Tomcat, Apache OpenNLP, Stanford CoreNLP - o Machine Learning Toolkits: WEKA, Mallet, (Conditional Random Fields, Hidden Markov Models, Support Vector Machines, C4.5 Decision Trees), Apache Mahout - o Natural Language Processing: NLTK, LingPipe, etc. Dictionary-based approaches (gazetteers), Rule based approaches (Hearst Patters, PoS parsing), Machine Learning approaches (HMM, CRF)

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