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Elsevier

Global STM publisher of journals.

Data Scientist 2

Applied Machine Learning +1Confirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • Experience in data science, machine learning, artificial intelligence, natural language processing, statistics, applied mathematics, computer science, or a related quantitative area.
  • Experience working with frontier large language models such as OpenAI GPT, Anthropic Claude, and Google Gemini, including fine-tuning large language models and/or small language models.
  • Strong Python skills and the ability to write clean, maintainable, well-tested code.
  • A solid grasp of machine learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, model selection, and performance measurement.
  • Experience working with structured, semi-structured, or unstructured data, especially large-scale text or content datasets.
  • Familiarity with common data science and machine learning tools such as Pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, or Matplotlib.
  • Ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with analytical thinking, problem-solving skills, and attention to quality.
  • Clear communication skills, a collaborative approach to working with engineering, product, and business stakeholders, and an interest in building production-ready systems that deliver user value.

Responsibilities

  • Design and build machine learning, natural language processing, and generative artificial intelligence systems for scientific discovery, knowledge extraction, decision support, and intelligent content understanding.
  • Work with large-scale, complex, and heterogeneous data, including scientific publications, research datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and content from every scientific discipline.
  • Apply suitable techniques to each problem, including classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, large language models, retrieval, and generative artificial intelligence.
  • Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization, question answering, and evidence-grounded generation.
  • Build, evaluate, fine-tune, prompt, and integrate models into robust production systems, while continuously improving quality, relevance, reliability, and user value.
  • Write clean, tested, production-quality Python and contribute reusable data science components, packages, and scalable data pipelines for preprocessing, inference, experimentation, monitoring, and continuous improvement.
  • Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems.
  • Collaborate with engineering, product, user experience, analytics, research, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations to technical and non-technical audiences.

About the Company

Elsevier

Global STM publisher of journals.

Elsevier provides scientific, medical, and technical content and analytics to researchers, educators, clinicians, and institutions worldwide through journals, books, databases, and digital platforms. It operates by offering access to peer-reviewed articles and professional literature via online platforms, along with tools for discovery, citation tracking, research management, and clinical decision support. Unlike other publishers, Elsevier combines a broad portfolio of STM content with integrated analytics and workflow tools, serving researchers, healthcare professionals, and academic institutions on a global scale. Its goal is to enable the advancement of knowledge and improve health outcomes by delivering reliable, accessible information and data-driven solutions across the research lifecycle.

Company size
10,001+
Company stage
Not listed
Total funding
Not listed
Headquarters
Amsterdam, Netherlands
Founded
1880

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