Lead, Data Science

Job Summary

We seek a senior software engineering leader to build and run the engineering backbone of our AI Center of Excellence (AI CoE). This role owns the end-to-end (E2E) delivery of Python-based AI platforms and products, from design and secure development through model/service deployment, production operations, governance, and lifecycle management in a regulated banking environment. You will lead multi-disciplinary team members to turn prototypes into compliant, scalable, reliable systems that create measurable business value.

• Define the AI engineering vision, roadmap, and operating model for the AI CoE; translate business priorities into executable, value-driven delivery plans.
• Establish standards for AI software engineering excellence: secure SDLC, architecture review, technical design, coding conventions, documentation, and inner-source practices.
• Drive a product mindset: clear backlogs, fast iteration cycles, and outcome-based KPIs.

• Architect and deliver reusable Python platforms (backend), ReAct/Vue (frontend) and reference services for:
  • LLM, RAG/Graph
    
    RAG services, agent orchestration, prompt/version management, evaluation-as-code, guardrails, and content moderation.
  • Data/feature/vector stores; retrieval pipelines;
    Text-to-SQL and tool-calling services.
• Own API and event-driven architectures (REST/gRPC/FastAPI; Kafka/Pub Sub), microservices, and front-end integration (React/Type Script/Next.js) for internal users (RMs, analysts) and operational teams.
• Ensure non-functional excellence: performance, cost, resiliency, observability, and operability (SLOs, error budgets).

• Lead the path from PoC → MVP → production, including change management, release governance, DR/BCP, capacity planning, and runbooks.
• Implement robust CI/CD & Git Ops (build/test/scan/sign/release) for Python services, models, and data pipelines across hybrid infra (Kubernetes/Open Shift; on-prem & cloud).
• Partner with model risk and validation to embed evaluation gates (offline/online AB tests, drift/guardrail monitors) before and after go-live.

• Ensure data privacy, secrets, access control, and lineage; enforce SAST/DAST/SCA, container/image signing, SBOMs, data contracts, and audit trails.
• Align delivery with bank governance (model risk, data privacy, records retention, third-party risk); streamline approvals with evidence-backed automation.
• Champion safe-by-design AI: red‑teaming, prompt‑injection defenses, eval suites, and incident playbooks.

• Stand up AI‑SRE practices: golden signals, Open Telemetry instrumentation, autoscaling, canary/blue‑green deploys, shadow traffic, and rollback strategies.
• Establish runtime cost/latency optimization (token budgets, caching, distillation/quantization, routing).

• Build and mentor high‑performing teams (managers, leads, ICs) across backend, data/ML, and front‑end disciplines.
• Partner with COE CPO, Data science team, CDO, AI platform, Security, Cloud, Data Platform, and Business Lines to unblock delivery; manage vendors/open‑source responsibly.

• Awareness and understanding of the Group's AI COE strategy and model appropriate to the role.

• Awareness and understanding of how the AI models operating in the Group.

• Responsible for developing some Machine Learning/GenAI models.

• The ability to interpret the Machine Learning models, identify key issues and put in place appropriate model controls and measures.

• Identify key Responsible AI related to the models developed.

• Display exemplary conduct and live by the Group’s Values and Code of Conduct.
• Take personal responsibility for embedding the highest standards of ethics, including regulatory and business conduct, across Standard Chartered Bank. This includes understanding and ensuring compliance with, in letter and spirit, all applicable laws, regulations, guidelines and the Group Code of Conduct.
• Effectively and collaboratively identify, elevate, mitigate and resolve risk, conduct and…