Director of NLP Engineering

Amgen is a biotechnology pioneer since 1980 committed to unlocking the potential of biology for patients suffering from serious illnesses in areas of high unmet medical need by discovering, developing, manufacturing and delivering innovative human therapeutics. This approach begins by using tools like advanced human genetics to unravel the complexities of disease and understand the fundamentals of human biology. Amgen has grown to be one of the world's leading independent biotechnology companies operating in over 100 countries, has reached millions of patients around the world and is developing a pipeline of medicines with breakaway potential. With its collaborative and respectful culture, Amgen cherishes being science-based as a top value and provides an environment for purposeful work to dramatically improve health outcomes and people's lives.

The Enterprise Engineering group at Amgen is part of the Information Systems function with the charter to accelerate Amgen's engineering capabilities across all geographies and business functions including Research & Development, Operations, Global Commercial Operations, and Commercialization. The Natural Language Processing (NLP) Engineering team in the Enterprise Engineering group is a central team of NLP scientists and engineers who develop solutions in the form of platforms for a variety of search related use-cases in the company. This team is currently looking for a Director of NLP Engineering, internally know as a Director of Data Sciece, to lead in further accelerating the rapid team growth and in delivering world-class NLP platforms. This is a great opportunity to help shape Amgen's rapidly evolving NLP capabilities in the golden age of biotechnology and no prior biotechnology experience is required! If you are a top NLP expert excited about making history at a top biotechnology company with top talent, top culture and top benefits, Amgen's NLP Engineering team is interested in partnering with you.

Responsibilities of the Director of NLP Engineering include:

• Growing, developing, and scaling a team of NLP scientists and engineers as well as providing hands-on guidance on best practices for design and implementation of cutting-edge NLP platforms.
• Applying NLP expertise to develop models and algorithms, perform exploratory research, and collaborate with scientists and engineers to productize new NLP platforms.
• Evolving the roadmap of the existing NLP platforms for search, crawlers, and document processing/enrichment.
• Operating as a technical consultant for and a trusted partner to the internal business stakeholders in order to identify, articulate, and drive opportunities that will enhance the capabilities of the NLP platforms in fostering data-driven executive decision making.
• Defining the R&D roadmap as a thought leader to shape next-gen NLP capabilities.

Basic Qualifications:

Doctorate degree and 4 years of NLP experience
OR
Master's degree and 8 years of NLP experience
OR
Bachelor's degree and 10 years of NLP experience
AND
4 years of managerial experience directly managing people and/or leadership experience leading teams, projects, programs or directing the allocation of resources

Preferred Qualifications:

• A Ph.D. degree in a quantitative field such as Statistics, Operations Research, Applied Math, Physics, Quantitative Finance, Computer Science or a related Engineering degree.
• Proven track record of specialized NLP expertise in the areas of linguistics, language-modeling grammar, natural language processing, and conversational flow and dialogues with relevant academic publications and recognition in industry venues.
• 10+ years of experience in managing direct reports in the industry and/or academia.
• 10+ years of experience in building Machine Learning and Deep Learning models and algorithms for NLP applications in the industry and/or academia.
• 5+ years of experience in building platform solutions as part of an engineering team.
• 5+ years of experience in managing and analyzing large scale structured and unstructured data using R and/or Python.
• Experience in using reference data management platforms for terminology management and leveraging of terminologies for search capabilities.
• Experience with semantic networks and knowledge management best practices.
• Experience in working directly with or in support of diverse business functions.
• Exposure to enterprise-wide platform development life-cycle in engineering teams.