Senior 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 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 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 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

Working for you

We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

Dutch Share Purchase PlanAnnual Profit Share BonusComprehensive Pension PlanHome, office or commuting allowanceGenerous vacation entitlement and option for sabbatical leaveMaternity, Paternity, Adoption and Family Care leaveFlexible working hoursPersonal Choice budgetVariety of online training courses and career roadshowsWellbeing programs and gym facility in the officeInternal communities and networksVarious employee discountsRecruitment introduction rewardWork from anywhereEmployee Assistance Program (global)Annual Event

About Data Science at Elsevier

Elsevier is one of the world’s leading publishers of trusted scientific, technology, and medical content. Building on that foundation of content, we leverage data science and AI to deliver knowledge and analytics products that advance science and improve healthcare outcomes. Therefore, an efficient, streamlined, and optimized data science development process is key to sustaining innovation and business growth, especially with the opportunities ahead with GenAI. Elsevier is committed to adhere to our RELX Responsible AI Principles, for the development and deployment of all of our AI solutions. 

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