AI Data Scientist

Job description

Career CategoryInformation SystemsJob DescriptionABOUT AMGEN Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller, and longer. We discover, develop, manufacture, and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains at the cutting edge of innovation, using technology and human genetic data to push beyond what is known today. ABOUT THE ROLE You will play a critical role in advancing Amgen’s Operations Transformation and Digital Strategy by designing and delivering next-generation AI solutions that modernize how data, knowledge, and content are created, connected, governed, and consumed across the enterprise, in collaboration with the Operations AI Transformation Team. This role is focused on applying Generative AI, agentic AI, integrated data, and semantic technologies to solve complex business problems in a highly regulated environment. The AI Data Scientist will help shape and scale AI-enabled solutions that support enterprise transformation, with particular emphasis on regulatory, operational, and data-centric use cases. This role will contribute to initiatives that combine modern AI capabilities with structured content, knowledge graphs, and interoperable data models to enable intelligent automation, improved decision-making, and new ways of working. This is an individual contributor role for a technically strong and strategically minded AI practitioner who can move from concept to proof of value and from prototype to scalable implementation while maintaining a strong commitment to responsible AI, compliance, and continuous improvement. Role Description The AI Data Scientist is responsible for designing, developing, and scaling AI-driven solutions that combine Generative AI, agentic AI, semantic data, and enterprise data platforms to create measurable business value. This role requires deep technical expertise, strong business engagement, and the ability to translate ambiguous business opportunities into production-oriented AI products and capabilities. With a focus on pharmaceutical and operational data domains, the role involves applying data science, ontology-based modeling, knowledge graph methods, and modern AI technologies to build intelligent systems that can reason over interconnected data, generate insights, and automate knowledge-intensive tasks. The role will work across structured and unstructured data, leveraging AWS, Databricks, OpenAI APIs, AWS Bedrock, and related platforms to deliver scalable and compliant AI solutions. This role will also help establish best practices in AI, support transformation initiatives through experimentation and applied innovation, and promote a culture of continuous improvement in AI product delivery and business adoption. Roles & Responsibilities Design, develop, and deploy AI solutions using Generative AI, large language models, and agentic AI patterns to address high-value business and operational challenges. Build intelligent solutions that combine structured and unstructured data, semantic data models, and knowledge graph capabilities to improve automation, search, insight generation, and decision support. Develop and scale proof-of-concept solutions in Python and transition them into robust, enterprise-ready AI products for broader organizational use. Apply SQL and other data transformation techniques to analyze, integrate, and prepare data from multiple enterprise sources. Collaborate with business stakeholders, product teams, data engineers, architects, and domain experts to identify AI use cases and translate business requirements into scalable technical solutions. Integrate FAIR data principles and data-centric design practices with AI to promote interoperability, discoverability, and governance of data assets. Leverage AWS services and cloud best practices, including AWS Bedrock, to design scalable, secure, and maintainable AI solutions. Develop AI-enabled applications using OpenAI APIs, foundation model platforms, and enterprise AI tooling in alignment with business and technical standards. Utilize Databricks and associated data/AI workflows to support experimentation, model development, and scalable deployment patterns. Ensure alignment with Responsible AI, security, privacy, and compliance expectations, including familiarity with emerging AI regulatory frameworks such as the EU AI Act. Facilitate working sessions with stakeholders to clarify concepts, define success criteria, and align on transformation opportunities enabled by AI. Contribute to the development of standards, reusable assets, and best practices for AI solution design, semantic modeling, and product scaling. Promote a culture of continuous improvement, innovation, and transformation by identifying opportunities to improve current processes, technologies, and ways of working. Stay current with advancements in AI, agentic architectures, and regulatory expectations, and apply those insights pragmatically within the organization. Basic Qualifications and Experience Master’s OR Bachelor’s degree with 4 to 7 years of experience in Data Science, Artificial Intelligence, Computer Science, Information Science, or related Functional Skills Must-Have Skills Strong hands-on experience in Generative AI, large language models, and agentic AI concepts and applications. Proficiency in Python for rapid prototyping, solution development, and scaling AI-driven capabilities. Strong working knowledge of SQL for data analysis, transformation, and integration across enterprise systems. Experience with AWS cloud services and well-architected cloud design best practices, including familiarity with AWS Bedrock. Experience using OpenAI APIs and related AI development patterns for enterprise solutions. Hands-on experience with Databricks or comparable data and AI platforms. Strong understanding of integrated data, enterprise data ecosystems, and data-centric solution design. Experience with knowledge graph and graph-based data platforms such as Stardog, GraphDB, or similar technologies. Strong understanding of FAIR data principles and their application in enterprise and pharmaceutical data environments. Ability to translate business concepts and requirements into scalable AI, semantic, and data solutions. Good-to-Have Skills Experience with regulatory data, regulatory submission processes, or compliance requirements in the pharmaceutical domain. Familiarity with the pharmaceutical lifecycle of data, including development, manufacturing, regulatory, and operational domains. Knowledge of machine learning and deep learning concepts, especially where they complement Generative AI use cases. Experience integrating data from sources such as clinical, laboratory, manufacturing, quality, or regulatory systems. Familiarity with healthcare and life sciences standards such as FHIR, IDMP, or related interoperability frameworks. Exposure to structured content management, content automation, or AI-enabled document workflows. Experience supporting AI solutions in highly regulated environments with strong governance expectations. Familiarity with product-oriented delivery and scaling AI proofs of concept into operationalized enterprise capabilities. Soft Skills Exceptional interpersonal, communication, facilitation, and business analysis skills. Strong analytical thinking and structured problem-solving skills, especially in complex and regulated environments. Ability to manage ambiguity, think strategically, and convert emerging opportunities into practical solutions. Strong ability to prioritize and manage multiple initiatives in a dynamic setting. Demonstrated customer- and user-centric product design mindset. Strong collaboration skills with cross-functional, global, and multidisciplinary teams. Ability to influence without authority and build alignment a