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Elsevier: Machine Learning Scientist


Seeking a Machine Learning Scientist to develop and apply machine learning methods in projects across Elsevier; use data analytics to support businesses and products; serve as internal and external specialist on Machine Learning techniques.



Company: ElsevierElsevier
Location: Amsterdam, Netherlands
Web: www.elsevier.com
Position: Machine Learning Scientist

_Contact_:
Email Sven Schroder.

Are you a Machine Learning Scientist and do you know of the state-of-the-art tooling in capturing content and translating human annotations to machine models? Are you familiar with deep learning algorithms and solutions? We have the right opportunity for you!

In line with the Elsevier corporate strategy of greater content volume, types and sophistication, the services that Elsevier provides are becoming increasingly dependent on Smart Content. We are therefore looking for a Machine Learning Scientist who can focus on designing and creating systems that enable machine learning in the context of article submission systems and other systems where authors or other human agents can enter metadata and other structured data to publications.

Focus of the position

As a Machine Learning Scientist you will be working with our business units on developing our content and information offering to end customers. These services rely on existing text and data mining as well as content structuring and meta-data generation processes. Many of these processes rely on human interaction and creation – and this will remain that way. We want to introduce more automation to these processes by capturing human inputs and back the submission and annotation systems by machine learning engines. Ultimately, machine learning tools may suggest annotations and structured meta-data that are as good as human-generated data and even replace human annotations. Our search solutions depend heavily on concept indexing or annotation, relationship extraction, or extracting data from images and tables. The ideal candidate will have industry experience solving meta-data-normalization problems using statistical methods on human-tagged data - and apply that experience to all of the above areas.

Working together

You will be working in Elsevier Operations with a varied group and cross-functional team of IT and product colleagues to pilot and develop new methods of extracting and surfacing information relevant to our customers for new product development. When successful, the Machine Learning Scientist will support the implementation of industry-scale high-quality production systems. You will work closely with both the publishing, content modelling and NLP teams. Sample projects may include analysis of content quality, article reference structuring / resolution, institution / author disambiguation, and concept / keyword suggestion and normalization and deep learning from annotated data.

Main Activities and Responsibilities

Develop and apply machine learning methods in projects across Elsevier

  • The suitable candidate will bring active experience in information extraction from structured and unstructured data and work on the projects that require large data processing
  • Applying and developing machine learning techniques, the Machine Learning Scientist will drive the implementation of automated recognition and annotation processes, in order to improve them in cost and time-efficiency
  • Using the available data, the Machine Learning Scientist will actively promote new ideas to enhance our competitive offerings
  • The Machine Learning Scientist will actively contribute to product and operational content strategies by identifying and ingesting new technical capabilities to forward Elsevier mission of leading the way in advancing science, technology and medicine

Use data analytics to support businesses and products

  • Analyze extracted information to drive such processes as automated and manual data cleansing
  • Use data analytics to drive decisions for our content acquisition strategy
  • Use visualization tools to present findings to stakeholders and other relevant parties

Serve as internal and external specialist on Machine Learning techniques

  • The suitable candidate will serve as the Machine Learning expert  in the Content and Innovation team
  • The Machine Learning Scientist will also be able to act as a liaison between IT developers and (content) subject matters experts, translating information needs into software development

Qualifications

  • Master or PhD degree in Computer Science, Engineering, Statistics, Data Science, Computational Linguistics or an associated area
  • A creative problem solver with a strong knowledge of statistics, text analytics and machine learning methods and strategies
  • Industry experience is strongly preferred
  • Ability to drive new developments and implement process changes and disruptive technologies in the organization
  • Good communication and documentation skills with the ability to convey complex technical concepts to non-technical professionals
  • Experience working with a variety of stakeholders at the mid and senior management level

Technical skills

  • Software development experience in a curly brace language or Python, as well as scripting abilities
  • Experience with kNN, SVM and other stochastic models for classification as well as CRF- type models for sequence recognition/ prediction
  • Writing queries, handling data (ETL), and experience using *nix systems, open source software and libraries
  • Proven experience with data normalization and processing, NLP, parsers, and spell checkers
  • Experience with Spark is a plus
  • Familiarity with agile software development
  • Experience with working in cloud based environments such as AWS is a plus
  • Support technical scoping, design solutions requirements and testing, maintain documentation and perform code reviews
  • Know how to improve efficiency of existing code and optimize performance

What we offer

We welcome you to a truly global, dynamic and challenging environment with great opportunities for personal development. Elsevier’s benefits are very competitive and the summary below will give you an idea of what you can expect when joining Elsevier in the Netherlands.

  • Competitive salary and a 13th month
  • 27 days of leave
  • Attractive collective health care insurance package with considerable reduction rates
  • Solid Pension Plan, with a choice between a collective pension plan an individual pension plan
  • Profit share or bonus plan subject to the company annual results
  • You can participate in the convertible personnel bond scheme
  • Flexible working arrangements
  • Travel allowance for commuting
  • Reductions to several personal insurance packages due to our collective agreements
  • Additional benefits, such as memberships to Elsevier’s magazines, discount on books and in-house sport facilities Numerous training, coaching and e-learning modules for long term job opportunities and development
  • Several local and global networking communities to share best practices and knowledge
  • Various social responsibility programs, channeling knowledge and strengths to help communities around the world improve education, science, health care and protect the environment.  

An assessment or business case could be part of our selection procedure. A pre-employment screening will be part of our recruitment procedure.

We are a global company headquartered in Amsterdam, employing more than 7,000 people in 24 countries. Today we are driving innovation by delivering authoritative content with cutting-edge technology, allowing our customers to find the answers they need quickly. We are part of the RELX Group plc., employing over 34.000 staff.       

A leading provider of science and health information, Elsevier partners with experts around the globe to develop world-class content, delivering it in ways that fuel discovery, drive innovation and improve health care. Our global community comprises over 7,000 journal editors, 70,000 editorial board members, 300,000 reviewers and 600,000 authors. They are scientists and clinicians; authors and editors, professors and students; information professionals and decision makers.       
Want to read more? Please visit: www.elsevier.com