Elsevier is hiring a Senior Data Scientist I

Elsevier is looking for a Senior Data Scientist I to develop and deploy Generative AI, RAG, Agentic AI, and NLP solutions for the health sciences domain. You will own the full lifecycle of data science projects, from design to productionization, with a focus on customizing RAG pipelines and championing ethical AI practices.

What You'll Do

• Collect data, perform analysis, develop models, define metrics, and conduct quality assessments, presenting findings to stakeholders.
• Create production-ready Python packages for components like pre-processing, model inference, and evaluation, coordinating deployment with technology teams.
• Design, develop, and deploy Generative AI models to meet specific business needs.
• Optimize and customize existing Retrieval Augmented Generation (RAG) pipelines for project requirements.
• Perform large-scale data ingestion, preprocessing, and transformation of multilingual content for high-quality model inputs.
• Build Agentic RAG systems.
• Work with AI agent management tools like LangChain, AutoGen, Haystack, or MCP.
• Fine-tune large language models (LLMs) and transformer models to enhance accuracy and relevance.
• Implement guardrails and evaluation mechanisms to ensure responsible and ethical AI usage.
• Conduct rigorous testing and evaluation of AI models to ensure high performance and reliability.
• Integrate data science components and ensure end-to-end quality assessment.
• Maintain pipeline robustness against model drift and ensure consistent output quality.
• Establish reporting for pipeline performance and develop automatic re-training strategies.
• Work collaboratively with cross-functional teams to integrate AI solutions into existing products.
• Mentor junior data scientists and contribute to team knowledge-sharing.
• Stay current with the latest advancements in AI, machine learning, and NLP.

What We're Looking For

• Master’s or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related field.
• 7+ 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 and fine-tuning techniques for large language models (LLMs).
• Proficiency in Generative AI technologies, including utilizing LLMs via API, evaluation tools, and prompt engineering.
• Knowledge of various RAG pipelines and their practical implementation.
• 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.
• Understanding of AI ethics, guardrail implementation, and evaluation metrics.
• Familiarity with cloud platforms like AWS, Azure, or Bedrock for deployment and pipeline creation.
• 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 *nix systems, open-source software, Jupyter Notebook, libraries, and cloud computing.
• Excellent problem-solving and analytical skills, with strong attention to detail.
• Strong communication skills and ability to work effectively in a team.

Nice to Have

• Experience building Agentic RAG systems.
• Experience with AI agent management tools like LangChain, AutoGen, Haystack, or MCP.
• Experience with end-to-end model deployment, leveraging AI agents, Model Context Protocol (MCP), and cloud platforms such as AWS (including Bedrock) or Azure.
• Experience in Java.

Technical Stack

• Languages & Frameworks: Python, Java
• AI/ML: Generative AI, LLMs, Transformer models, RAG, Agentic AI, NLP, Machine Learning, Deep Learning, Neural Networks, Reinforcement Learning, Transfer Learning, Random Forests, SVM, Logistic Regression, Bayesian Modelling
• Tools & Platforms: LangChain, AutoGen, Haystack, MCP, AWS, Azure, AWS Bedrock, GitLab, GitHub, Jira, Jupyter Notebook, Cloud Computing

Team & Environment

You will be part of the Data Science Health team.

Benefits & Compensation

• Health insurance for you and your family, with enhanced options at competitive rates.
• Group life insurance and group accident insurance.
• Flexible working arrangements for work-life balance.
• Employee assistance programs for personal and work-related support.
• Medical screenings.
• Modern family benefits, including maternity, paternity, and adoption support.
• Long-service awards and new baby gifts.
• Subsidized meals at specific locations.
• Various paid time-off options, including casual, sick, privilege, compassionate, and special sick leave, plus public holidays.
• Free transportation for home-office-home travel in select locations.
• Healthy work/life balance initiatives and shared parental leave.
• Study assistance and sabbaticals.

Elsevier is 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.