Data Scientist / Senior Data Scientist

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Date: Mar 29, 2023

Location: Heidelberg, DE

Company: Springer Nature Group

Springer Nature opens the doors to discovery for researchers, educators, clinicians and other professionals. Every day, around the globe, our imprints, books, journals, platforms and technology solutions reach millions of people. For over 175 years our brands and imprints have been a trusted source of knowledge to these communities and today, more than ever, we see it as our responsibility to ensure that fundamental knowledge can be found, verified, understood and used by our communities – enabling them to improve outcomes, make progress, and benefit the generations that follow. 

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Springer Nature is seeking a Data Scientist (all levels are welcome to apply) for its highly-regarded Data and Analytics Solutions group. You will be working within the newly founded Data Capability Centre on all aspects of the data-value creation chain for our scientific data solutions. We are looking for a passionate and creative data scientist who is motivated to take a critical role in the creation of industry-strength solutions that unleash the value of data, innovating in how we transform the traditional document perspective into a world of knowledge.


You will build and apply machine-learning models on real problems that matter, combine them with rule-based approaches where appropriate, analyse scientific data of different kinds from a variety of domains, as well as leverage knowledge models and knowledge graphs to consume and create scientific facts and new knowledge. You will support the business via actionable insights enabling new opportunities. You will work with a pool of highly talented and engaged data scientists & engineers, domain experts, and software engineers. You will have opportunities to work with the latest data and technologies including graph databases, Google BigQuery, Tensorflow and Hugging Face – you’ll name what’s the right tool for your job and our objectives.


Role responsibilities will include:

  • Build machine learning models and conduct data analysis that support Springer Nature and its customers to advance discovery
  • Implement and innovate cutting edge AI and data-science solutions in partnership with the wider team, sift through plenty of scientific texts and data permanently looking for the insights that help our business to move the needle
  • Work in a fail-fast PoC driven environment with plenty of opportunities to move successful prototypes towards production
  • Consult with stakeholders throughout the business and the Data Capability Centre
  • Collaborate with other data scientists and experts as well as product-development teams on different scientific domains
  • Communicate and stand in for your work and achievements, influence decisions and technologies


Role requirements:

  • University degree with a strong analytical/quantitative background or equivalent experience (e.g. Data Science, Statistics, Mathematics, Econometrics, Physics, Computer Science etc.)
  • Strong working knowledge of Python, other languages (Java, Scala, etc.) is a plus
  • Excellent analytical problem solving capabilities
  • Ability to visualise and tell stories from data
  • Demonstrable experience with AI/ML, NLP,  DL, preferably also statistics and coding
  • Experience of using cloud platforms (GCP, Azure, etc.), Kubernetes, and graph databases (RDF-based triple stores and/or property graphs) is a plus
  • Well organized and accurate with good time management, able to work independently as well as in strong dynamic teams

Preferred location: Heidelberg, Germany

At Springer Nature, we value the diversity of our teams and work to build an inclusive culture, where people are treated fairly and can bring their differences to work and thrive. We empower our colleagues and value their diverse perspectives as we strive to attract, nurture and develop the very best talent.

Springer Nature was awarded Diversity Team of the Year at the 2022 British Diversity Awards. Find out more about our DEI work here.


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