Chief Risk and Data Officer

Overview

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.

Experience

• Hand-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.
• Hand-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.