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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Amgen - **Location:** Washington, DC, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Business Analytics Applications, Data Analysis, Big Data, Cloud Computing, Cloud Database, Data Validation, Data Governance, Data Infrastructure, Data Mining, Python (Programming Language), Machine Learning, Natural Language Processing, Search Technologies, Software Engineering, SQL Databases, Enterprise Software Applications, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, AI Platforms, Core Data, Information Technology, Data Analytics, Machine Learning Operations, Software Version Control - **Published:** October 12, 2026 - **Apply:** https://dejobs.org/x/x/5127633878FE481E94F35EA938B7B8AB/job/ ## About the Role * Master's degree and 1 to 3 years of Computer Science, IT or related field experience OR * Bachelor's degree in data science, business, statistics, data mining, applied mathematics, business analytics, engineering, computer science, or a related field, with 2 years of relevant experience. OR * Associate's degree in data science, business, statistics, data mining, applied mathematics, business analytics, engineering, computer science, or a related field, with 6 years of relevant experience * Hands-on experience with Python and SQL for data analysis, feature engineering, machine learning, natural language processing, and AI solution development * Experience designing or developing GenAI and large-language-model capabilities such as AI assistants, retrieval-augmented generation, embeddings, semantic search, prompt engineering, or agentic workflows * Strong foundation in machine learning, statistical analysis, exploratory data analysis, time-series methods, and model evaluation * Experience progressing data science or AI solutions from experimentation into production, including testing, version control, monitoring, and MLOps or LLMOps practices * Ability to work with large, complex datasets and collaborate on data modeling, data quality, lineage, and reusable analytical data pipelines * Demonstrated ability to translate business problems into technical approaches, independently lead a technical workstream, and communicate results clearly, * Experience with supply chain, manufacturing, or biotech analytics use cases such as demand and supply planning, inventory, capacity, cost, financial planning, or operational KPIs * Experience with cloud-based data and AI platforms, large-scale data processing, and integration of AI capabilities into enterprise applications * Understanding of responsible AI, AI governance, security, compliance, observability, and ongoing solution performance management * Experience supporting analytical or reporting products and working with cross-functional teams and delivery partners in an Agile or SAFe environment Professional Certifications: * Data Science, Artificial Intelligence, or Machine Learning Certification (Preferred) * Cloud or Data Platform Certification (Optional) Soft Skills: * Excellent critical-thinking, analytical, and problem-solving skills * Strong communication, collaboration, and stakeholder-management skills * Demonstrated ability to provide technical leadership and influence without formal authority * Ability to connect technical work to business outcomes and explain complex topics clearly * Strong presentation, knowledge-sharing, and mentoring skills ## Description Let's do this. Let's change the world. In this vital role you will serve as a core data science expert and AI-enabled capabilities within Amgen's Global Supply Chain Data & Analytics team. The role will focus primarily on designing, developing, evaluating, deploying, and continuously improving AI assistants, agentic AI capabilities, and advanced analytics solutions supporting the analytics ecosystem. This individual will partner with product owners, business stakeholders, data engineers, software engineers, architects, and delivery partners to translate complex supply chain problems into secure, scalable, and reliable solutions that improve user productivity and business decision-making. The ideal candidate combines hands-on data science and GenAI expertise, business understanding, stakeholder communication, and experience operationalizing solutions in an enterprise environment. Roles & Responsibilities: * Serve as the data science technical lead and subject matter expert for assigned AI-enabled capabilities across analytics capabilities * Partner with product owners and supply chain stakeholders to translate business problems into prioritized AI and analytics use cases, technical approaches, success measures, and delivery plans * Design, develop, evaluate, and enhance GenAI and agentic AI solutions using large language models, retrieval-augmented generation, semantic search, prompt engineering, and multi-agent workflows * Establish evaluation and monitoring approaches for AI solutions, including accuracy, relevance, robustness, safety, user feedback, and appropriate human review * Apply machine learning, statistical modeling, and time-series analysis to supply chain use cases involving demand, supply, inventory, capacity, cost, risk, and operational performance * Collaborate with data engineers to develop trusted analytical datasets, reusable features, data quality checks, lineage, and documentation supporting AI and analytics products * Work with software engineering, architecture, platform, testing, and delivery partners to integrate data science capabilities into enterprise applications and support testing, deployment, production performance, and issue resolution * Provide technical guidance and reviews to data scientists, engineers, and delivery partners; coordinate dependencies and promote reusable approaches across connected products * Develop technical documentation, operating procedure and contribute to technical reviews * Communicate findings, solution tradeoffs, risks, recommendations, and business impact to technical and non-technical stakeholders while contributing through the Agile delivery model and applicable AI and data governance processes