Join a high-impact analytics and AI team building scalable products that turn data into measurable business outcomes. Work hands-on across the full lifecycle—from problem framing to deployment—while applying modern GenAI and agentic AI patterns responsibly. Grow your career by delivering secure, auditable solutions that drive adoption, efficiency, and insight at scale.
As an Analytics & Agentic AI Product Lead within the Data & Analytics organization, you deliver analytics products and AI capabilities from discovery through implementation, adoption, and performance monitoring. You translate business needs into analytical requirements, success metrics, and roadmaps while building robust data foundations and dashboards. You design and deploy LLM/agentic AI use cases with governance, evaluation, and human-in-the-loop controls.
**Job Responsibilities **
- Own end-to-end delivery of analytics products from framing through adoption and monitoring.
- Partner with stakeholders to translate needs into requirements, success metrics, and delivery roadmaps.
- Build and optimize data pipelines, datasets, and dashboards with SQL and strong analytics engineering practices.
- Implement data quality checks, documentation, and lineage to ensure auditability and trust.
- Develop Python analytics solutions including wrangling, modeling, experimentation, forecasting, and segmentation.
- Apply NLP techniques where relevant to improve insight generation and text-based workflows.
- Design and deploy agentic AI use cases with guardrails, audit trails, and human-in-the-loop controls.
- Prototype automated insight generation, ticket triage, research assistants, and data quality agents.
- Establish testing and evaluation frameworks covering accuracy, robustness, bias/risks, drift, and business KPIs.
- Integrate AI solutions with enterprise systems, APIs, workflow tools, and operational platforms.
- Monitor solution performance and iteratively optimize models, prompts, and workflows for impact.
**Required Qualifications, Capabilities, and Skills **
- Demonstrate strong Python programming skills and API integration experience.
- Apply hands-on expertise in distributed data processing and large-scale data engineering.
- Utilize advanced SQL and data modeling concepts to build reliable datasets.
- Build and orchestrate AI/LLM-powered applications using RAG and vector-based semantic search.
- Engineer prompts, embeddings, and LLM optimization techniques to improve quality and efficiency.
- Implement AI governance, monitoring, observability, and responsible AI practices across deployments.
- Deploy and manage ML solutions with MLOps, CI/CD, version control, and secure scalable architecture.
**Preferred Qualifications, Capabilities, and Skills **
- Leverage experience with agentic AI systems and multi-agent orchestration.
- Apply knowledge of transformer architectures and modern generative AI frameworks.
- Utilize workflow orchestration tools and event-driven architectures to scale solutions.
- Bring experience in financial services domains such as regulatory reporting, risk, or enterprise operations.
- Implement real-time analytics, streaming pipelines, and incremental processing frameworks.
- Strengthen data governance with lineage, auditability, and access control practices.
- Improve retrieval systems using vector databases, feature engineering, and semantic search tuning.