Associate Director, Data Scientist

AI Operations, Contractor Delivery & Hands-On Technical WorkWorks as part of a team responsible for managing contractors, technical delivery partners, and AI workstreams across applied AI initiatives. Supports contractor onboarding, work planning, technical direction, delivery coordination, quality review, and accountability for assigned work. Provides hands-on technical direction for AI prototypes, model development, application patterns, data pipelines, and production AI systems. Reviews technical designs, architecture decisions, model evaluation plans, code quality, implementation tradeoffs, and production-readiness. Contributes to prototypes, proof-of-concepts, notebooks, design documents, technical spikes, and code reviews when needed. Promotes scientific rigor, reproducibility, engineering excellence, responsible AI practices, documentation, and maintainable delivery patterns.AI Research, Applied Innovation & Product ValueLeads development, evaluation, deployment, and scaling of AI capabilities supporting research, development, clinical, regulatory, safety, and enterprise use cases. Applies product thinking to ensure AI solutions address clear user needs, workflow realities, business priorities, adoption goals, and measurable outcomes. Partners with Product Management & Experiences to understand user needs, prioritize opportunities, define success metrics, and support adoption. Uses experimentation, user feedback, benchmarking, and iterative delivery to validate assumptions and improve AI capabilities over time. Identifies opportunities to use emerging AI technologies to accelerate scientific discovery and operational effectiveness.Technical Architecture & Engineering ExcellenceDesigns and guides AI solution architectures for assigned projects and business domains. Guides development of Retrieval-Augmented Generation systems, agentic workflows, prompt and context engineering patterns, evaluation harnesses, model monitoring, and Langfuse-based observability. Sets expectations for production-quality code, automated testing, version control, reproducible experiments, scalable deployment patterns, and operational documentation. Develops reusable AI frameworks, tools, accelerators, platforms, and services that enable faster delivery across Research, Development, and enterprise functions. Helps troubleshoot complex issues across data quality, model behavior, latency, reliability, security, scalability, cost, compliance, and user experience.Responsible AI, Governance & Production OperationsEnsures AI solutions follow applicable governance, privacy, security, regulatory, and responsible AI expectations. Implements practical approaches for Large Language Model evaluation, groundedness assessment, hallucination risk management, traceability, and quality measurement. Uses platforms such as Langfuse or equivalent approved tooling for LLM tracing, debugging, prompt and response analysis, observability, evaluation workflows, and production monitoring. Supports Machine Learning Operations, Large Language Model Operations, continuous integration and delivery, model monitoring, observability, and operational support practices.Collaboration & Stakeholder EngagementCollaborates with Product Management & Experiences, Business Delivery Excellence, Enterprise AI & Governance Excellence, ARC translational AI teams, and Development partners. Partners with scientists, therapeutic area leaders, clinical teams, regulatory functions, Information Technology, Privacy, and Drug Development Systems to prioritize high-impact AI opportunities. Communicates technical concepts, product strategy, risks, tradeoffs, and delivery progress clearly to technical and non-technical audiences. Contributes to ARC initiatives that advance AI capabilities, governance, adoption, product innovation, and operational excellence.Requirements Minimum Education & ExperiencePhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Statistics, Engineering, or related discipline with 4+ years of relevant industry experience. MS in a related discipline with 8+ years of relevant experience. BS in a related discipline with 10+ years of relevant experience. Demonstrated expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Generative AI, or related disciplines. Experience guiding technical contributors, contractors, or cross-functional project teams, including technical direction, coaching, delivery oversight, and quality review. Hands-on experience building, evaluating, and deploying AI or machine learning solutions in applied research, product, or enterprise environments.Core Technical RequirementsStrong hands-on programming skills in Python and practical experience with modern AI and machine learning libraries such as PyTorch, TensorFlow, or equivalent approved technologies. Experience in clinical trial operational data, real-world data, and supporting trial feasibility, site selection and forecasting. Experience building Generative AI applications with LangChain, LangGraph, Semantic Kernel, Microsoft Agent Framework, Langfuse, AWS-native AI services, Microsoft Azure services where appropriate, or equivalent approved enterprise technologies. Experience designing and implementing Retrieval-Augmented Generation systems, including chunking, embeddings, vector search, reranking, grounding, citation patterns, retrieval evaluation, and response quality measurement. Experience developing agentic AI workflows, tool-use patterns, orchestration approaches, guardrails, human-in-the-loop review models, prompt engineering, context engineering, and model evaluation techniques. Experience with APIs, microservices, notebooks, Git-based development, automated tests, containerization, deployment patterns, monitoring, and operational support for AI systems. Experience deploying AI solutions in regulated environments with appropriate governance, security, privacy, compliance, scalability, reliability, and responsible use controls.AI Domain ExpertiseStrong knowledge of Generative AI, Large Language Models, advanced analytics, and applied machine learning. Experience in one or more of the following areas: foundation models, multimodal AI, agentic AI systems, scientific machine learning, knowledge graphs, Retrieval-Augmented Generation, Natural Language Processing, or advanced deep learning. Experience designing evaluation frameworks for Large Language Models, including accuracy, groundedness, hallucination risk, robustness, latency, cost, safety, user acceptance, and Langfuse-based tracing or evaluation workflows. Experience translating research and experimental AI concepts into scalable production capabilities.Cloud, Engineering & Operations StackExtensive experience with Machine Learning Operations, Large Language Model Operations, continuous integration and delivery, cloud-based AI infrastructure, observability, model monitoring, and automated testing. Experience working with Amazon Web Services and Microsoft Azure, including AI, machine learning, data, security, and scalable compute services. Experience using Langfuse or equivalent approved tooling for LLM application tracing, debugging, prompt and response analysis, production observability, evaluation workflows, and quality measurement. Experience applying software engineering methodologies, scalable AI architectures, reusable components, documentation standards, and maintainable production systems.Product, Leadership & Business AcumenDemonstrated product mindset with experience translating technical capabilities into solutions that deliver measurable user and business value. Experience partnering with product managers.