Scrum Lead

Job description

The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterprise level solutions for D&A Operations. In this role, the individual partners with Operations and Technology teams to design data driven solutions that deliver business and customer value. The position requires expertise in data analytics, NLP, deep learning, and data communication, along with ability to learn financial content and core D&A business processes. The individual must know the latest technologies—including Generative AI, Multi-modal AI, LLMOps practices, AI Agents, and synthetic data techniques—and collaborate with operations groups and specialized machine‑learning teams to deliver scalable, innovative, and high‑impact solutions. Role, Responsibilities & Key Accountabilities: Strategic & Technical Skill • Hands‑on technical expertise across end‑to‑end data science initiatives, ensuring high‑quality design, development, and delivery. • Shape and refine the product vision for advanced data management and analytics frameworks spanning data acquisition, transformation, quality, and workflow automation. • Define optimal user experiences for financial analytics pipelines, integrating diverse tools, datasets, and services into cohesive workflows. • Own and drive large projects from data science perspective, removing obstacles and own creative solutions. • Serves technical authority upholding standard, and architecture recommendations. Business & Stakeholder Engagement • Partner with domain SME and business owners to identify high‑value problems and co‑create AI/ML and platform strategies. • Translate business requirements into technical specifications, solution designs, and measurable success criteria. • Communicate complex insights, findings, and solution outcomes to product, engineering, sales, proposition, support, and leadership teams. • Influence multi-functional teams by providing clear, data‑driven recommendations and technical direction. Advanced AI/ML Delivery & Emerging Technologies • Strategize, build, and optimize production‑grade AI models—including deep learning, NLP, large language models, and Retrieval‑Augmented Generation (RAG). • Demonstrate expertise with LLMOps and advanced MLOps frameworks, including vector databases, orchestration tools (e.g., LangChain, LlamaIndex), and scalable model‑serving platforms to handle end‑to‑end LLM lifecycle • Demonstrate strong expertise Generative AI and Multi-modal AI advancements, including models that handle text, images, audio, and video in unified architectures, significantly reducing pipeline complexity • Assess third‑party AI technologies, frameworks, and tools to decide build‑versus‑buy decisions and strengthen platform capabilities. • Establish and uphold high coding standards, reproducibility practices, and quality controls for robu