Data Scientist

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 Corporate Markets is a diverse team focusing on GenAI, ML, NLP. We mainly develop best-in-class enrichment pipelines for Elsevier’s corporate markets products such as Reaxys, Embase and Pharmapendium.

About the Role

As a Data Scientist, you will support the development, evaluation, and deployment of AI and NLP models used in our products. You will contribute to building and optimizing components of our GenAI, RAG, and NLP pipelines, working closely with senior data scientists, software engineers, and subject matter experts in chemistry and biology.

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 is ideal for candidates with foundational knowledge of NLP/ML who are eager to learn, take ownership of tasks, and grow into more advanced responsibilities.

Responsibilities

• Assist in collecting, cleaning, and preparing structured and unstructured data for model development.
• Contribute to ML/NLP model prototyping and evaluation, including defining basic quality metrics with guidance.
• Support optimization of Retrieval Augmented Generation (RAG) components such as document ingestion, retrieval, and preprocessing.
• Help design and test model inference pipelines, transformer-based models, and GenAI applications.
• Develop well-structured, production-ready Python modules for preprocessing, model execution, and evaluation.
• Collaborate with developers to integrate data science components into production workflows.
• Support end-to-end quality validation of deployed pipelines and help monitor model performance over time.
• Stay informed about emerging ML and NLP techniques and tools relevant to ongoing projects.
• Communicate results in a clear, structured way to technical and non-technical stakeholders.
• Work with senior team members on both independent tasks and small-scale project ownership.

Requirements

• MSc/MTech in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Bioinformatics, or a similar quantitative field OR Bachelor’s degree + 1–2 years of relevant experience.
• Some applied experience with ML or NLP projects (internships, academic work, or industry roles).
• International study or work exposure is a plus.
• Solid Python programming skills for data analysis and model development.
• Basic familiarity with transformer models and modern NLP techniques.
• Experience using LLMs via APIs, prompt engineering, or experimentation with GenAI tools.
• Understanding of RAG concepts and willingness to learn how to implement them.
• Exposure to cloud environments (AWS, Azure, Bedrock) or interest in learning deployment workflows.
• Familiarity with common ML algorithms (e.g., logistic regression, SVMs, random forests) and model evaluation practices.
• Experience with GitHub/GitLab and Agile ways of working.
• Strong analytical and problem-solving mindset.
• Curiosity and willingness to learn new technologies.
• Clear communication and ability to work in a collaborative, cross-functional team.

Nice-to-Have Skills

• Experience with agent frameworks (LangChain or similar).
• Familiarity with Databricks, OpenSearch.
• Knowledge of deep learning fundamentals (e.g., neural networks, transfer learning).
• Basic understanding of software engineering practices and CI/CD workflows.

Work in a way that works for you

We promote a healthy work/life balance across the organization. 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.

• Flexible working hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.