Data Scientist II

Reposted 18 Days Ago
2 Locations
In-Office
Mid level
Artificial Intelligence • Healthtech • Information Technology • Other • Analytics
The Role
Design and build ML, NLP, and generative AI systems for scientific discovery and knowledge extraction. Work with large heterogeneous scientific datasets to develop semantic search, entity extraction, QA, summarization, embeddings, and production-ready models. Implement model evaluation, fine-tuning, deployment, monitoring, drift detection, and scalable data pipelines while collaborating across engineering, product, UX, and research teams.
Summary Generated by Built In

Data Scientist, London/Oxford hybrid working

Are you excited by the opportunity to use machine learning, NLP, and generative AI to help researchers discover knowledge faster and make better decisions?

Would you enjoy turning complex scientific and business challenges into practical, production-ready AI solutions that create real user value?

About our Team

Our global team support products education electronic health records that introduce students to digital charting and prepare them to document care in today’s modern clinical environment. We have a very stable product that we’ve worked to get to and strive to maintain. Our team values trust, respect, collaboration, agility, and quality.

About the Role

In this role, you will design and build machine learning, NLP, and generative AI solutions that support scientific discovery, knowledge extraction, decision support, and intelligent content understanding. You will work with large-scale scientific content and data, applying the right techniques to solve complex problems and deliver reliable, production-ready systems. Working closely with cross-functional partners, you will help turn ambiguous challenges into measurable outcomes that improve how researchers discover and use knowledge.

Responsibilities

  • Design and build machine learning, NLP, and generative AI 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 the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI.
  • 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, UX, analytics, research, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical audiences.

Requirements

  • Experience in data science, machine learning, artificial intelligence, NLP, statistics, applied mathematics, computer science, or a related quantitative area.
  • Experience working with frontier LLMs such as OpenAI’s GPTs, Anthropic’s Claude, and Google’s Gemini, including fine-tuning LLMs and/or SLMs.
  • Strong Python skills and a habit of writing 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.
  • The ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with strong analytical thinking, problem-solving skills, and attention to quality.
  • Clear communication skills, a collaborative approach to working with engineering, product, and business stakeholders, and a genuine interest in building production-ready systems that deliver real user value.

Work in a Way That Works for You

We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance, and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.

Working Pattern

Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive

About the Business

A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better worl

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.

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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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Skills Required

  • Experience in data science, machine learning, AI, NLP, statistics, applied math, or related quantitative area
  • Experience working with frontier LLMs (OpenAI GPTs, Anthropic Claude, Google Gemini) including fine-tuning
  • Strong Python skills; ability to write clean, maintainable, well-tested code
  • Solid grasp of ML fundamentals (supervised/unsupervised learning, feature engineering, model evaluation, model selection)
  • Experience with large-scale structured, semi-structured, or unstructured text/content datasets
  • Familiarity with data science tools: Pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, Matplotlib
  • Experience building semantic search, retrieval, embeddings, ranking, recommendation, summarization, QA, and evidence-grounded generation
  • Experience deploying and maintaining production ML systems, including monitoring, drift detection, and automated retraining
  • Strong analytical problem-solving, ability to translate ambiguous requirements, and clear communication with cross-functional stakeholders

Elsevier Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Elsevier and has not been reviewed or approved by Elsevier.

  • Leave & Time Off Breadth Feedback suggests paid time off spans vacation, holidays, sick days, bereavement, military leave, and volunteer time. Family-related leave options are also emphasized as part of the package.
  • Healthcare Strength Feedback suggests medical, dental, vision, life insurance, wellness initiatives, and an EAP form a comprehensive health offering. Gym support and wellbeing hubs reinforce an ongoing health and wellness focus.
  • Retirement Support Feedback suggests retirement programs include a 401(k)/retirement plan and pension options, with long-term savings vehicles such as an employee stock purchase plan also available. These elements contribute to a sense of financial security beyond base pay.

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The Company
HQ: Amsterdam
Year Founded: 1880

What We Do

Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, and deliver better care. Because informed decisions lead to better outcomes, Elsevier is a leader in information and analytics for customers across the global research and health ecosystems. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We do this by facilitating insights and critical decision-making for customers across the global research and health ecosystems.

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