Data Scientist II

About The Role

In this role, you will design and build machine learning, NLP, and generative AI solutions that support clinical knowledge discovery, evidence extraction, decision support, and intelligent content understanding. You will work with large-scale clinical and scientific content and data, applying the right techniques to solve complex problems and deliver reliable, production-ready systems. Working closely with cross-functional partners, you will help turn ambiguous clinical and scientific challenges into measurable outcomes that improve how clinicians and researchers discover and apply knowledge.

About The Team

As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline.

Responsibilities

• Design and build machine learning, NLP, and generative AI systems for clinical knowledge discovery, evidence extraction, decision support, and intelligent content understanding.
• Work with large-scale, complex, and heterogeneous data, including clinical documentation, scientific publications, research datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and content spanning every clinical and scientific discipline.
• Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI.
• Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization, question answering, and evidence-grounded generation for clinical and scientific use cases.
• Build, evaluate, fine-tune, prompt, and integrate models into robust production systems, while continuously improving quality, relevance, reliability, and clinical/user value.
• Write clean, tested, production-quality Python and contribute reusable data science components, packages, and scalable data pipelines for preprocessing, inference, experimentation, monitoring, and continuous improvement.
• Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems that clinicians and researchers depend on.
• Collaborate with engineering, product, UX, analytics, research, clinical, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical audiences.

Requirements

• Experience in data science, machine learning, artificial intelligence, NLP, statistics, applied mathematics, computer science, or a related quantitative area.
• Experience working with frontier LLMs such as OpenAI's GPTs, Anthropic's Claude, and Google's Gemini, including fine-tuning LLMs and/or SLMs.
• Strong Python skills and a habit of writing clean, maintainable, well-tested code.
• A solid grasp of machine learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, model selection, and performance measurement.
• Experience working with structured, semi-structured, or unstructured data, especially large-scale text or clinical/scientific content datasets.
• Familiarity with common data science and machine learning tools such as Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, or Matplotlib.
• The ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with strong analytical thinking, problem-solving skills, and attention to quality.
• Clear communication skills, a collaborative approach to working with engineering, product, clinical, and business stakeholders, and a genuine interest in building production-ready systems that improve health outcomes.

Why This Work Matters

Your models won't just process data, they'll help shape how clinicians document care, how students learn to practice medicine, and how researchers uncover insights that improve patient outcomes. This is AI applied where it counts.

Benefits

• Healthy work/life balance initiatives.
• Wellbeing programs and parental leave.
• Study assistance for long-term career goals.
• Country-specific benefits tailored to your location.

Pay

U.S. National Base Pay Range: $71,600 - $119,400. Geographic differentials may apply in some locations to better reflect local market rates. This job is eligible for an annual incentive bonus.