Lead - Data/AI & Analytics

Primary Job Responsibilities:
- Lead the design, development, and production deployment of analytics and machine learning models, including enterprise fraud risk management (EFRMS) and transaction monitoring use cases, from problem definition through sustained performance in production.
- Set detection thresholds and calibrate models by institution, managing the trade-off between detection coverage and false positives in consultation with risk and business stakeholders.
- Own model documentation, validation evidence, and regulatory reporting for all models in production, ensuring audit readiness at all times.
- Own the data platform and data lakehouse roadmap, covering the enterprise data model, ingestion standards, transformation layers, data quality, and lineage.
- Be accountable for data availability, timeliness, and fitness for purpose across all analytics, AI, and management reporting use cases.

- Lead the data engineering and analytics team, setting technical standards, code and model review practices, and delivery discipline.
- Work with delivery partners and vendors on platform build, integration, and enhancement, holding them to technical specification and agreed timelines.
- Translate business and regulatory requirements into a prioritised technical backlog, and communicate progress, risks, and trade-offs clearly to senior stakeholders.
Professional Skills:
- 7+ years across data engineering, analytics, or data science, including time spent leading a technical team.
- Hands-on experience with modern data platforms and lakehouse architecture, distributed processing (Spark or equivalent), and SQL at scale.
- Practical experience building and productionising machine learning models, ideally in fraud, financial crime, or credit risk.
- Understanding of transaction and account data in banking, including core banking and payment system data structures.
- Experience with model risk management practices, including independent validation, monitoring, drift detection, and documentation.
- Familiarity with data quality frameworks, metadata management, and lineage tooling.
- Exposure to cloud data services, workflow orchestration, and CI/CD for data and machine learning pipelines.
- Ability to hold technology vendors to technical specification and delivery commitments.
Educational Qualifications:
- B. Tech / B.E / MCA / Masters in Statistics, Data Science, Computer Science or equivalent.