Lead, Data Science Job Description

Key Responsibilities

Leadership & Strategy
•    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.
Platform & Architecture (Python Full-Stack)
•    Design and implement reusable Python modules in the backend
•    LLM, RAG/GraphRAG services, agent orchestration, prompt/version management, evaluation frameworks, guardrails, and content moderation
•    Data/feature/vector stores; retrieval pipelines; Text-to-SQL and tool-calling services
•    Develop API and event-driven architectures (REST/gRPC/FastAPI; Kafka/PubSub), microservices, and front-end integration (React/TypeScript/Next.js) • Engineer for non-functional excellence: performance optimization, cost efficiency, resiliency, observability, and operability (SLOs, error budgets)

E2E Productionization in Banking
•    Execute technical implementation from PoC → MVP → production, including code development, testing, and deployment automation 
•    Build robust CI/CD & GitOps pipelines (build/test/scan/sign/release) for Python services, models, and data pipelines across hybrid infrastructure (Kubernetes/OpenShift; on-prem & cloud) 
•    Implement evaluation frameworks (offline/online A/B tests, drift/guardrail monitors) and model validation systems
•    Develop automated testing suites, performance benchmarks, and monitoring solutions
Security, Risk & Compliance
•    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.
Operations & SRE for AI
•    Direct and help 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).
 People & Stakeholder Management
•    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.

Strategy  
•    Deep technical understanding of AI CoE strategy and ability to translate into engineering solutions

Business 
•    Technical comprehension of AI model operations and their business applications within the Group

Processes   
•    Hands-on development and implementation of Machine Learning/GenAI models and supporting infrastructure

People & Talent 
•    Technical mentoring and knowledge sharing with engineering teams

Risk Management 
•    Technical implementation of ML model controls, monitoring systems, and risk mitigation measures

Governance 
•    Engineering of Responsible AI controls and compliance automation for developed models

Regulatory & Business Conduct

•    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