Chief Risk and Data Officer

Our client is an innovative fintech project based in Saudi Arabia, building a next‑generation peer‑to‑peer lending platform aimed at serving underbanked and thin‑file populations. The platform expands access to fair, transparent, technology-driven credit while creating new investment opportunities for retail and institutional lenders.

The team is building the entire ecosystem from scratch using a modern microservices architecture and a real‑time data infrastructure. This is a rare opportunity to shape a greenfield lending platform in a rapidly growing, well-capitalised environment with strong vision and ambitious scaling plans across emerging markets.

The Chief Risk & Data Officer (CRDO) is a mission‑critical executive role responsible for unifying risk, modelling, data architecture and governance into one high‑impact function. The CRDO owns the full lifecycle of alternative‑data decisioning, portfolio‑risk management, enterprise data strategy, and regulator‑ready governance.

This leader will design and scale a fully auditable, real‑time, data‑driven lending engine capable of operating in emerging markets with initially minimal data and gradually expanding to rich alternative‑data environments.

Key Responsibilities

Risk & Decision Science Leadership

• Build end‑to‑end credit‑decision engines for thin‑file and micro‑loan lending.
• Develop dynamic risk‑based pricing, approval strategies, and behavioural scorecards.
• Design and maintain real‑time PD/LGD models, portfolio‑risk dashboards, and EWS triggers.
• Manage the full model‑risk governance cycle, including documentation and audit trails.
• Align Product, Engineering, Data and Collections teams around unified risk limits and target ROE.
• Oversee portfolio monitoring, NPL caps, loss forecasting and scenario modelling.

Data & Architecture Ownership

• Define and deliver the company’s real‑time data architecture:

Streaming ELT → data lake/warehouse → feature store → ML‑ops pipeline.

• Make strategic architectural choices (e.g., Kafka vs. Kinesis, Delta vs. Iceberg).
• Ensure robust data quality, lineage and metadata management using tools such as:

Great Expectations, DataHub, Collibra.

• Build and scale company‑wide BI, reporting, KPI frameworks and data literacy programs.
• Support engineering in building scalable, compliant microservices for credit and data operations.

Leadership & Team Management

• Build and lead multi‑disciplinary teams: risk analysts, data engineers, ML engineers, BI analysts.
• Mentor future leaders and drive a high‑performance, safety‑first, analytically rigorous culture.
• Ensure strong cross‑functional collaboration across Product, Engineering, Finance, Collections and Operations.

Требования

Experience

• Hands‑on experience building credit‑risk engines, pricing models and data pipelines for thin‑file or micro‑loan lending.
• Ability to work initially with minimal data (phone + ID only) and scale into rich alt‑data ecosystems (device, telco, behavioural, psychometric, open banking).
• Commercial instinct to balance conversion rate, data cost and decision quality, managing approval‑rate vs. portfolio‑yield trade-offs.
• Experience in regulated environments with a clean compliance reputation.
• Hands‑on coding in Python or R, strong SQL, and understanding of ML‑ops latency/production constraints.
• Demonstrated experience presenting frameworks to regulators and auditors.

Skills & Competencies

• Deep understanding of portfolio management: vintage curves, NPL, ROE, collections strategy.
• Strong architectural judgement: streaming, storage formats, orchestration, ML‑ops.
• Ability to link dashboards, models and KPIs directly to commercial OKRs.
• Strong cross‑functional communication and executive‑level storytelling.
• Ability to build scalable teams in fast‑moving, ambiguous environments.

Mindset

• Hands‑on, pragmatic, commercially minded.
• Scientific but not academic — every model tied to P&L.
• Zero tolerance for compliance shortcuts.
• High ownership, high integrity, high urgency.