Senior Credit Risk Analyst (PJN

Senior Credit Risk Analyst - Pay Just Now
Purpose of the role:

This role exists to turn fragmented data into profitable lending decisions. The business originates credit across two very different products — a high-volume, thin-margin, short-tenor BNPL book and a longer-dated retail instalment loan book — and the data that should inform those decisions currently sits in silos: bureau feeds in one system, transactional and behavioural data in another, merchant and collections data elsewhere again.

The Senior Credit Risk & Decision Scientist is accountable for three connected outcomes:

• Data optimisation across silos — building a single, governed, decision-ready view of the customer by integrating bureau, internal and alternative data sources, and eliminating duplicated or unused data spend.
• Scorecard build and monitoring — developing, validating, deploying and continuously monitoring application, behavioural, affordability and collections scorecards across both product lines.
• Account origination — designing and optimising the end-to-end origination decision engine: policy rules, cut-offs, limit assignment, affordability assessment, fraud screening and champion/challenger strategy.

Data Optimization across business silo's:

• Map the full credit data estate across origination, servicing, collections, merchant/partner and marketing systems; document lineage, ownership, refresh cadence, quality and cost of every source.
• Design and own a consolidated credit data mart / feature store that serves modelling, decisioning and reporting from a single set of definitions, removing conflicting versions of the same metric.
• Integrate and rationalise the three data families the business depends on:
  • Bureau data — scores, enquiry data, tradeline and payment-profile history, adverse and judgment data, affordability indicators.
  • Internal data — application, transactional, repayment and arrears behaviour, customer tenure, cross-product holdings, merchant and basket-level data, servicing and contact history.
  • Alternative data — bank transaction / open-banking data, device and digital footprint, telco and payment-behaviour signals, geospatial and psychometric indicators where lawful and predictive.
• Run a continuous cost-to-value assessment of every paid external data source: quantify incremental lift per Rand of bureau or alternative-data spend and retire, renegotiate or re-sequence calls that do not pay for themselves.
• Implement tiered / cascaded data-call strategies so expensive attributes are only purchased where they change the decision.
• Define and enforce data quality standards, reconciliation controls and monitoring for all decision-critical feeds; own the resolution path when a feed degrades or a bureau attribute drifts.
• Partner with Data Engineering to product ionise pipelines, and with Product and Finance so that a single agreed set of risk and profitability metrics is used across the business.

• Own the full model lifecycle for application, behavioural, affordability, fraud-propensity and collections scorecards across BNPL and retail loan portfolios.
• Construct reliable modelling samples: target definition, performance and outcome windows, exclusions, reject inference, and correction for the short performance windows and rapid repeat-usage cycles typical of BNPL.
• Engineer and select features from bureau, internal and alternative sources; apply appropriate binning, WoE/IV analysis, segmentation and multicollinearity treatment.
• Build models using the right tool for the job — logistic regression and scorecard scaling where explainability and regulatory defensibility are required; gradient boosting and other machine-learning techniques where lift justifies them, supported by explainability output (SHAP or equivalent).
• Validate rigorously: out-of-time and out-of-sample testing, Gini/KS/AUC, calibration, stability, segment-level performance and fairness/disparate-impact testing.
• Produce model documentation to internal model-risk and audit standard, and present models to the Credit or Model Risk Committee for approval if needed.
• Support deployment into the decision engine and sign off implementation…