Senior Data Scientist - AI Engineering - Clinical Applications

Novo Nordisk•Lexington, MA

About The Position

The Senior Data Scientist I contributes to the research team’s efforts to explore and create new technology and advance data science. The role combines data collection, preparation and engineering with software engineering in a data science context, and develops and validates algorithms and models with a high degree of initiative and self-sufficiency. The position also guides more junior Data Scientists. The role reports to the Associate Director of the Clinical AI development area. Day-to-day collaborators include Data Scientists, AI Engineers, and System Architects. The position also works with medical professionals, clinical trial teams, and biostatisticians to ensure AI/ML solutions address clinical needs and drug development workflows. The Senior Data Scientist develops LLM-based solutions for healthcare and drug development, helps engineer AI products, and builds data solutions such as vector databases, embedding pipelines, and knowledge bases. The role collaborates on the design, development, testing, deployment, and iteration of machine learning systems; explores prompting, fine-tuning, and optimization methods; documents models and their maintenance; and shares knowledge across teams. The position stays current with GenAI/LLM research and technology and contributes to cross-functional teamwork and team capability.

Requirements

Bachelor’s degree required; a degree in a relevant subject area is preferred.
A Bachelor’s degree with 5 or more years of relevant experience, a Master’s degree with 3 or more years of relevant experience, or a PhD with no experience may be considered.
Hands-on experience with Azure Databricks.
Experience in LLMs and generative AI, including model selection, system design, fine-tuning, prompt engineering, evaluation, and observation.
Foundational understanding of deep learning and reinforcement learning models and architectures, such as transformers, GANs, and VAEs.
Proven Python programming skills and familiarity with data science and ML tools such as Jupyter Notebook, HuggingFace, and PyTorch.
Experience building data pipelines and retrieval systems, including vector databases, embedding pipelines, and knowledge bases, to support RAG, graphs, and document-understanding workflows.
Ability to execute Data Science tasks while mentoring more junior Data Scientists.
Excellent written and oral communication skills.
Detail-oriented with strong documentation skills.
Ability to develop and implement sound technical solutions to complex problems.
Ability to work independently and in teams and collaborate with external parties.

Nice To Haves

Master’s degree or PhD.
Experience in the pharmaceutical or healthcare industry, regulated medical device development, or another regulated field.
Experience deploying agentic solutions to tackle healthcare problems.
Familiarity with CI/CD best practices, cloud-based distributed systems, containerization, and test automation, particularly for stateful LLM system designs.
Experience designing agentic solutions for healthcare problems, including considerations for evaluation and harnessing.

Responsibilities

Develop and validate LLM-based solutions for content retrieval, generation, summarization, and inference for healthcare and drug development challenges.
Support software engineering for AI products by architecting services, designing APIs, building backend systems, and integrating with frontend applications while meeting performance, safety, privacy, and ethics requirements.
Build and maintain data solutions, including vector databases, embedding pipelines, and knowledge bases, to support RAG and document-understanding workflows.
Collaborate with Data Scientists to design, code, train, test, deploy, and iterate on machine learning systems.
Apply prompt engineering, model fine-tuning techniques, and optimization methods such as quantization, distillation, and caching.
Create technical documentation, runbooks, and training materials for model use and maintenance.
Accomplish objectives with a high degree of self-sufficiency and collaborate with Data Scientists and engineers from other disciplines on daily tasks.
Participate in cross-functional teams developing AI and ML solutions.
Stay current on GenAI/LLM research, best practices, and the technology roadmap.
Share learnings, knowledge, and skills; promote cross-functional teamwork; and help raise team competency by seeking and sharing Data Science and therapy-area knowledge.
Mentor more junior Data Scientists in developing their skills.