Senior/Principal NLP Scientist, Veeva

• 2+ years of experience as an NLP Engineer
• Recent graduates with a Ph.D. in NLP may also be considered
• Fluent in Python and best coding practices, and experience with bashing script
• Strong analytical skills and data curiosity
• Good English and strong communication skills
• Experience in at least one framework for BigData (e.g., Ray, Spark) and one framework for Deep Learning (e.g., PyTorch, JAX)
• Mastery of Machine Learning, including up-to-date knowledge of the latest ML innovations and frameworks
• Used to start-up environments and Agile mindset: quick iteration over ideas
• Proficiency in tackling complex problems
• Social competence and a team player
• Driven, self-motivated, and enthusiastic about the impact of their work on Veeva and society
• Background in Medical NLP
• Having published in AI space in a peer-reviewed journal
• Cloud development, especially AWS
• Experience with NoSQL databases, especially MongoDB

Desirable

• You will contribute to a new Link product focused on large-scale semantic analysis and question/answering over heterogeneous documents from the medical domain, such as academic publications, clinical regulation, and therapeutic committee meeting notes
• You will design and create an end-to-end pipeline to extract predefined categories of information from big unstructured data in multi-domain and multi-lingual setups
• You will create a semantic search capability that can answer user queries regarding certain aspects of data
• You will have access to cloud infrastructure to develop your model and support from a team of software developers and DevOps engineers to deploy it to production for various regions, languages, and medical specialties
• You train ML models empowered by over 2000 curators to meet both the quality and the scale requirements
• Develop machine learning models at scale and in AWS cloud to structure and analyze textual data
• Adopt the latest technologies and trends in NLP to your solution
• Fine-tune and serve Large Language Models
• Use and develop named entity recognition, entity-linking, slot-filling, few-shot learning, question/answering, dense passage retrieval, layout-informed models, and other statistical techniques and models for information extraction and machine reading
• Deeply understand and analyze our data model per data source and geo-region and interpret model decisions
• Collaborate with data quality teams to define annotation tasks, metrics, and perform qualitative and quantitative evaluations, and implement human-in-the-loop processes
• Collaborate with engineering teams to deploy models to production