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.
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.
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.
About the business
As 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 world.
Primary Location Base Pay Range: NLD Amsterdam (Radarweg) €53,800 - €89,900. This role is covered by the Collective Labor Agreement Publishing Industry.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.
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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
- Master's or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or related field.
- 5+ years of applied experience in data science with focus on Generative AI, NLP, and machine learning.
- Proficiency in Python for data analysis, model development, and deployment.
- Strong experience with transformer models.
- Proficiency with Generative AI technologies, including using LLMs via APIs and LLM evaluation tools.
- Knowledge and practical implementation experience with Retrieval Augmented Generation (RAG) pipelines.
- Experience building Agentic RAG systems (strong requirement).
- Experience with AI agent management frameworks such as LangChain or similar tools.
- Experience with advanced deep learning algorithms, neural networks, reinforcement learning, and transfer learning.
- Familiarity with traditional ML algorithms (random forests, SVM, logistic regression, Bayesian modelling) for model building and validation.
- Familiarity with cloud platforms for model deployment and production pipelines (e.g., Amazon Bedrock, AWS, Azure).
- Proficiency in data visualization tools and techniques.
- Experience with version control systems (GitLab or GitHub), Jira, and working in an Agile environment.
- Proficient in using OpenSearch and Databricks.
- Excellent problem-solving, analytical, communication skills, and ability to work in a team-oriented environment.
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.
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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.
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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.
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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.
Elsevier Insights
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.
