Senior Credit Risk Analyst (PJN)

About Weaver Weaver FinTech Group is a leading digital financial services ecosystem connecting consumers to smarter, more flexible ways to pay, borrow, and protect what matters most. Our brands Pay Just Now and FinChoice empower millions of South Africans through innovative Payments, Lending, and Insurance solutions. Were a data-first organisation built on collaboration, innovation, and purpose: to make digital finance simpler, more inclusive, and more human. 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. Success is measured commercially. The role is expected to demonstrably improve risk-adjusted return — higher approval rates at constant or lower loss, lower cost of data acquisition, and faster decisioning — not simply to produce models. Key Responsibilities: 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 productionise pipelines, and with Product and Finance so that a single agreed set of risk and profitability metrics is used across the business. Scorecard Development: 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 testing — confirming that the scored production population matches the development specification, attribute for attribute. Scorecard monitoring and model governance: 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 testing — confirming that the scored production population matches the development specification, attribute for attribute. Account Origination and Decision Strategy: Own the origination decision logic end-to-end: pre-screen, identity and fraud checks, bureau and alternative data calls, scorecard execution, affordability assessment, policy rules, cut-off setting, limit assignment and referral treatment. Optimise approval-rate versus loss trade-offs using swap-set analysis; quantify the profit impact of every proposed cut-off or policy change before it goes live. Design initial credit limit and limit-increase strategies for BNPL, balancing basket-size conversion and merchant experience against exposure at risk. Build and run a disciplined champion/challenger and A/B testing programme, including test design, sample sizing, holdout maintenance and readout, so that strategy changes are evidence-based rather than opinion-based. Reduce friction and decision latency: increase straight-through-processing rates, cut manual referrals, and shorten time-to-decision without increasing loss. Work with Fraud to separate first-party and third-party fraud losses from credit losses, and ensure origination strategy treats them differently. Feed origination insight back into Product, Merchant and Marketing teams — which channels, merchants, baskets and segments originate profitable accounts, and which do not. Commercial and Profitability impact: Translate analytics into commercial recommendations with quantified rand-value impact, and defend them to Credit, Finance and Exco. Contribute to credit loss forecasting and to the annual budget and business planning cycle. Other: Act as the analytical authority in Credit Committee forums; present clearly to non-technical audiences. Mentor junior analysts, review their code and models, and raise the analytical standard of the team.