Senior Data Scientist

About this role

Basic Qualifications:

• 5+ years of hands-on experience developing and fine-tuning ML or LLM models
• Demonstrated expertise in Python, with experience and knowledge of a commercial framework like PyTorch.
• Hands-on experience developing, managing, and troubleshooting workflows within Databricks for data engineering, analytics, and machine learning projects
• Documented strong understanding of the ML lifecycle
• Experience with embeddings and retrieval-augmented generation (RAG)

Preferred Qualifications:

• PhD in Machine Learning, Computer Science, or a related quantitative discipline.
• Previous experience excelling in a fast-paced, applied research setting where experimentation, iteration, and roadmap alignment are critical.
• Experience with causal inference, simulation modeling, or graph-based reasoning applied to clinical development or biomedical research.
• Hands-on fluency in Databricks notebooks for exploratory analysis, model development, and workflow orchestration.
• Curiosity for how AI training and inference performance impacts both user experience and downstream business value.
• Mindset of continuous learning, with the ability to bridge experimental work and high-value product applications.

Job Responsibilities:

• Focus on understanding existing models, assessing their performance, selecting optimal architectures, and fine-tuning them to meet specific domain and business needs—including retrieval-augmented generation (RAG) based applications.
• Collaborate closely with data engineering, product, and domain teams to translate real-world research challenges into scalable, model-driven solutions that accelerate vision of a digitally connected research data and technology fabric.
• Optimize and fine-tune large language models (LLMs) and domain-specific variants using proprietary datasets to achieve precision and recall targets that drive differentiated customer value.
• Evaluate model performance across key metrics and benchmarks, identi...