Elsevier
Data Scientist III

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Are you interested in working with data and analytics to solve problems? Are you interested in bringing your GenAI, ML and NLP expertise to projects?
About our Team
Data Science Life Sciences is a diverse team focusing on GenAI, ML, NLP. We mainly develop best-in-class enrichment pipelines for Elsevier’s life science .com products such as Reaxys, Embase and Pharmapendium.
About the Role
As a Senior Data Scientist, you will play a pivotal role in the development and deployment of cutting-edge Gen AI models and solutions. You will be responsible for building, testing, and maintaining our Gen AI, RAG and NLP solutions.
You will work throughout the whole life cycle of data science projects: design, implementation, production and beyond. You will deliver efficient and production-ready Python code. You will collaborate closely with developers to deploy and productionize our data science pipelines and with subject matter experts in biology and chemistry domains to validate the output.
This role requires a strong foundation in Natural Language Processing (NLP), Machine Learning, Transformer models and Generative AI, as well as proficiency in Python.
Responsibilities
- Data collection, data analysis, model development, defining quality metrics, quality assessment of models and regular presentations to stakeholders.
- Creating production-ready Python packages for each component of data science pipelines (such as pre-processing and model inference) and their deployment together with software engineering team.
- Optimizing and customizing Retrieval Augmented Generation (RAG) pipelines to meet specific project requirements that involve content ingestion, machine translation, and contextualized information retrieval.
- Ingesting, preprocessing, and transforming large-scale multilingual data to ensure high-quality inputs for downstream models.
- Building AI agentic models integrated with RAG pipelines.
- Conducting rigorous testing and evaluation of AI models to ensure high performance and reliability.
- Integrating data science components and performing end-to-end quality assessments.
- Maintaining robustness of data science pipelines against model drift and ensuring consistent output quality.
- Establishing reporting processes for pipeline performance and developing automated re-training strategies for existing pipelines.
- Collaborating with cross-functional teams to integrate AI solutions into existing products and services.
- Leading and managing projects with a team of data scientists and independently executing the entire small-scale projects.
- Mentoring junior data scientists and fostering a knowledge-sharing culture within the team.
- Staying up-to-date with the latest advancements in AI, machine learning, and NLP technologies.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Requirements
- Master’s or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related field.
- 5+ years of relevant applied experience in data science, with a focus on Generative AI, NLP, and machine learning.
- Proficiency in Python for data analysis, model development, and deployment.
- Strong experience with transformer models.
- Proficiency in Generative AI technologies, including utilizing LLMs via API access, LLM evaluation tools, and prompt engineering.
- Knowledge of various RAG pipelines and their practical implementation.
- Experience building Agentic RAG systems is strong requirement.
- Experience with AI agent management frameworks such as LangChain, or similar tools.
- Experience with advanced algorithms in deep learning, neural networks, reinforcement learning, and transfer learning.
- Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing.
- Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment and the creation of production-ready pipelines.
- Proficiency in data visualization tools and techniques.
- Experience with version control systems (e.g., GitLab or GitHub), Jira, and working in an Agile environment.
- Proficient in using OpenSearch and Databricks.
- Excellent problem-solving and analytical skills, with strong attention to detail.
- Strong communication skills and the ability to work effectively in a team-oriented environment.


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Primary Location Base Pay Range: NLD Amsterdam (Radarweg) €49,000 - €81,700. This role is covered by the Collective Labor Agreement Publishing Industry.
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