Data Scientist

Responsibilities

• Design and deploy machine learning, NLP, and generative AI solutions that help researchers discover, understand, and apply scientific knowledge.
• Build intelligent retrieval, search, recommendation, ranking, and question-answering systems that improve research outcomes.
• Develop AI systems that connect information across publications, datasets, citations, knowledge graphs, and scientific ontologies.
• Fine‑tune, evaluate, and integrate large language models and retrieval‑augmented generation (RAG) systems into production environments.
• Create robust evaluation frameworks that measure quality, reliability, relevance, trustworthiness, and user impact.
• Build scalable data pipelines and machine learning workflows that support experimentation, monitoring, and continuous improvement.
• Apply the appropriate combination of classical machine learning, deep learning, retrieval, and generative AI techniques to solve complex scientific problems.
• Collaborate with engineering, product, UX, analytics, and domain experts to transform ambiguous challenges into practical solutions.
• Contribute clean, maintainable, production‑quality Python code and reusable AI components.
• Continuously improve the capabilities, performance, and real‑world value of AI systems that support scientific discovery.

• Degree in Data Science, Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
• Extensive Python programming skills and experience building production-quality data science solutions.
• Experience with machine learning fundamentals, including model development, evaluation, feature engineering, and performance optimization.
• Experience working with large-scale structured, semi-structured, or unstructured datasets.
• Hands‑on experience with modern AI technologies, including large language models, embeddings, retrieval systems, and generative AI.
• Familiarity with frameworks such as Scikit-learn, PyTorch, Tensor Flow, Hugging Face, or equivalent tools.
• Experience evaluating AI outputs and improving model quality, reliability, and business impact.
• Ability to translate complex problems into measurable, data-driven solutions.
• A genuine passion for advancing science, improving access to knowledge, and using AI to create meaningful real‑world impact.