At Elsevier, we are looking for a Senior Data Scientist II to join our Data Science Health team. In this role, you will be responsible for developing and deploying advanced Generative AI, RAG, and Agentic AI solutions specifically for the health sciences domain, taking projects from initial design through to production.
What You'll Do
- Collect data, perform analysis, develop models, and present findings to stakeholders.
- Build and deploy production-ready Python packages for each stage of data science pipelines.
- Design, develop, and deploy Generative AI models to meet specific business needs.
- Optimize and customize Retrieval Augmented Generation (RAG) pipelines.
- Perform large-scale data ingestion and preprocessing of multilingual content.
- Construct Agentic RAG systems.
- Utilize AI agent tools like LangChain, AutoGen, Haystack, or MCP.
- Fine-tune large language models (LLMs) and transformer models.
- Implement guardrails and evaluation mechanisms to ensure ethical AI usage.
- Conduct rigorous testing and evaluation to ensure model performance and reliability.
- Integrate data science components and manage end-to-end quality assessment.
- Maintain pipeline robustness against model drift and ensure consistent output quality.
- Establish pipeline performance reporting and automatic re-training strategies.
- Collaborate with cross-functional teams to integrate AI solutions into 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 applied data science experience, with a focus on Generative AI, NLP, and machine learning.
- Proficiency in Python for analysis, development, and deployment.
- Strong experience with transformer models and LLM fine-tuning techniques.
- Proficiency in Generative AI technologies, including LLM APIs, evaluation tools, and prompt engineering.
- Practical knowledge of various RAG pipeline implementations.
- Experience with deep learning, neural networks, reinforcement learning, and transfer learning.
- Familiarity with traditional machine learning algorithms like random forests, SVM, and logistic regression.
- Understanding of AI ethics, guardrail implementation, and evaluation metrics.
- Familiarity with cloud platforms like AWS, Azure, or Bedrock for model deployment.
- Proficiency in data visualization tools and techniques.
- Experience with version control systems (GitLab/GitHub), Jira, and Agile workflows.
- Proficient in using *nix systems, open-source software, Jupyter Notebook, and cloud computing.
- Excellent problem-solving, analytical skills, and attention to detail.
- Strong communication and teamwork abilities.
Nice to Have
- Experience with end-to-end model deployment, including AI agents, Model Context Protocol (MCP), and cloud platforms like AWS Bedrock or Azure.
- Experience in Java.
Technical Stack
- Python, Generative AI, LLMs, Transformer models, NLP, Machine Learning
- RAG, Agentic AI, LangChain, AutoGen, Haystack, MCP
- AWS, AWS Bedrock, Azure, GitLab, GitHub, Jira
- *nix systems, Jupyter Notebook
Team & Environment
You will be part of the Data Science Health team at Elsevier.
Benefits & Compensation
- Health insurance for you and your family.
- Enhanced health insurance options at competitive rates.
- Group life and group accident insurance.
- Flexible working arrangements.
- Employee assistance programs.
- Medical screenings and modern family benefits (maternity, paternity, adoption).
- 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.
- Free transportation for home-office-home travel in select locations.
- Healthy work/life balance, well-being initiatives, shared parental leave, study assistance, and sabbaticals.
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.
