Data Scientist II

Overview

In this role, you will design and build ML, NLP, and generative AI solutions to accelerate scientific discovery and knowledge extraction. You’ll work with large-scale, heterogeneous scientific content and collaborate across engineering, product, UX, analytics, and domain experts to deliver production-ready systems. You will translate ambiguous research challenges into measurable, data-driven outcomes that improve how researchers find and use knowledge. This position offers the chance to shape AI-enabled discovery at a trusted, collaborative organization with a focus on quality and impact.

Pay / Benefits

• flexible working hours
• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• career development opportunities

Responsibilities

• Design and build ML, NLP, and generative AI systems for discovery, knowledge extraction, decision support, and content understanding
• Work with large-scale data including publications, datasets, knowledge graphs, ontologies, and metadata
• Apply a range of techniques (classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, generative AI)
• Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization, QA, and evidence-grounded generation
• Build, evaluate, fine-tune, prompt, and integrate models into production systems with ongoing quality improvements
• Write clean, tested Python code and develop reusable data science components, pipelines for preprocessing, inference, experimentation, monitoring, and CI/CD
• Support deployment, monitoring, model maintenance, drift detection, automated retraining, and optimization
• Collaborate with cross-functional teams and communicate model behavior, insights, and trade-offs to technical and non-technical audiences

Key requirements

• Experience in data science, ML, AI, NLP, statistics, applied mathematics, computer science, or related quantitative area
• Experience with frontier LLMs (e.g., GPTs, Claude, Gemini), including fine-tuning LLMs/SLMs
• Strong Python skills and well-tested code
• Solid grasp of ML fundamentals (supervised/unsupervised learning, feature engineering, evaluation, selection, performance)
• Experience with structured, semi-structured, or unstructured data, especially large-scale text or content datasets
• Familiarity with Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, or Matplotlib
• Ability to translate complex requirements into practical, data-driven solutions with strong analytical thinking and attention to quality
• Clear communication and collaborative mindset with stakeholders to deliver production-ready value

• clear communication
• collaboration
• analytical thinking
• Pandas
• NumPy
• SciPy

…