Senior Specialist - Decision Scientist

Job description

Job Title: Senior Specialist - Decision Scientist GCL: D3 Introduction to role: Are you ready to turn brand new AI into real-world outcomes that speed decisions, build strategy, and ultimately improve patients’ lives? As a Senior Specialist, you will be a hands-on technical leader who builds production-grade AI and ML solutions that power critical decisions across the enterprise. You will join a fast-moving, collaborative team that connects data, engineering, and product thinking to solve complex problems at scale. From LLM-powered applications and RAG pipelines to robust MLOps, you will translate ambitious ideas into reliable systems with measurable business value. Do you thrive on taking prototypes to production and making smart trade-offs that balance innovation with reliability? Accountabilities: Develop intelligent systems and machine learning models by building, testing, and deploying them. These models improve analytics capabilities and produce measurable business outcomes. Priority is given to solutions the business adopts and uses. Hands-On Engineering: Write production-quality code, build APIs and data pipelines, and integrate AI workflows using modern engineering practices so solutions are secure, maintainable, and scalable. Generative AI and LLMs: Develop LLM-based applications, RAG solutions, agents, and prompt strategies; evaluate models and orchestrate AI applications to deliver accurate, timely, and trusted insights. Productionization and MLOps: Move solutions from experimentation to reliable production with CI/CD, testing, versioning, system tracking, insight gathering, and operational controls that keep systems healthy over time. Technical Architecture: Chip in to scalable, secure architectures designed to handle machine learning tasks; make pragmatic technology choices that optimize performance, cost, and maintainability. Data and Platform Integration: Partner closely with platform, data engineering, and decision science teams to ensure solutions have reliable data, compute, tooling, and cloud infrastructure. Technical Ownership: Own end-to-end technical delivery for prioritized initiatives, translating organizational and data insights needs into robust builds and de-risking dependencies early. Cross-Functional Collaboration: Collaborate alongside data scientists, developers, product groups, and interested parties to deliver usable, scalable solutions aligned to real business needs. Continuous Improvement: Assess emerging AI technologies and engineering practices, finding opportunities to improve performance, reliability, scalability, and cost-effectiveness. Responsible AI and Governance: Build solutions that meet security, privacy, information management, and regulatory requirements, and apply responsible AI practices where AI is deployed in regulated environments. Essential Skills/Experience: • 7–10 years of experience in software engineering, machine learning engineering, AI engineering, data science engineering, or a closely related technical field. • Shown experience building and deploying production AI/ML applications, rather than solely leading AI projects or teams. • Strong programming skills in Python and familiarity with modern software engineering practices, including Git, testing, APIs, code reviews, and CI/CD. • Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, or equivalent. • Hands-on experience developing generative AI / LLM applications, including areas such as RAG, embeddings, vector databases, prompt engineering, agents, model evaluation, and LLM application integration. • Experience deploying AI/ML solutions in at least one major cloud environment (Azure, AWS, or GCP). • Practical experience with MLOps / LLMOps, including model or application deployment, monitoring, evaluation, versioning, observability, and automation. • Experience working with data pipelines, databases, APIs, and cloud-based data/analytics platforms. • Ability to translate business and analytics requirements into practical technical designs and production-ready solutions. • Strong understanding of software architecture, scalability, security, reliability, and performance considerations for AI workloads. • Experience working collaboratively with data scientists, data engineers, software engineers, product teams, and business collaborators. • Strong problem-solving skills and the ability to independently own technical workstreams from prototype through production. Desirable Skills/Experience: • Experience delivering AI/ML platforms and analytics products in cloud environments using modern MLOps or equivalent experience and LLMOps practices. • Experience with Azure OpenAI, OpenAI APIs, Amazon Bedrock, Google Vertex AI, or equivalent enterprise AI platforms. • Experience with orchestration and agent frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent technologies. • Experience with vector databases and retrieval technologies such as Pinecone, Azure AI Search, FAISS, pgvector, or equivalent. • Experience building AI applications using Docker, Kubernetes, serverless architectures, or other cloud-native technologies. • Experience with AI evaluation frameworks, model monitoring, observability, and techniques for improving model quality, latency, and reliability. • Experience optimizing cloud and AI workload costs and performance, including model selection, inference optimization, caching, and architecture optimization. • Experience working in pharma, biotech, healthcare, or other regulated industries, with an un