Latest Jobs at Weaver Fintech Ltd

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.

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Role Overview:

• The Team Leader: Document Processing is accountable for the daily leadership, coordination, and performance delivery of a team of Document Processing Consultants. This includes overseeing document collection and validation workflows, ensuring compliance and turnaround time targets are met, and driving a culture of continuous improvement and professional development. The role demands a strong focus on coaching, quality assurance alignment, and cross-functional collaboration to ensure optimal service delivery.

Key Responsibilities:

Operational Performance & Workflow Management

• Monitor and manage daily document collection and validation volumes.
• Drive productivity against defined targets (e.g., hourly throughput, online time, contact attempts).
• Escalate workflow bottlenecks or system issues to relevant support functions.
• Ensure process adherence across all customer contact and processing touchpoints.
• Conduct daily planning of activities and resource allocation based on volume and SLA.
• Maintain visibility and accuracy of dashboards and reports reflecting real-time and historical team performance.
• Collaborate with MRU TL to ensure cross-site coordination and support during overflow periods.
• Facilitate weekly operational alignment sessions with the Operations Manager.
• Support the accurate and timely execution of daily operational tasks including wrong number management, validations, and salvage preparation.
• Maintain a flexible approach to team scheduling and coverage.
• Participate in ad hoc projects or pilots as required.

People Leadership & Performance Management

• Conduct daily huddles, check-ins, and weekly one-on-ones.
• Set KPAs and development plans for each team member.
• Address performance challenges through real-time coaching and formal performance management channels.
• Foster accountability and maintain high levels of team morale and engagement.

Quality, Compliance & Coaching Alignment

• Work closely with the Skills Coach and QA team to identify trends and action coaching interventions.
• Support quality calibration sessions to ensure alignment in feedback standards.
• Ensure consultants adhere to compliance standards, including regulatory requirements and internal policies.

Reporting & Insight Generation

• Track daily, weekly, and monthly performance metrics.
• Analyse adverse trends and implement corrective actions in partnership with leadership.
• Maintain accurate coaching and performance logs.
• Provide structured feedback to operations and process leadership.

Cross-Functional Collaboration

• Liaise with L&D, QA, Process Analysts, and IT to resolve pain points and optimise team enablement.
• Escalate customer journey issues and propose solutions.
• Participate in initiatives to improve customer experience and operational efficiencies.

Digital Channel Support & Enablement

• Support team readiness and effectiveness in handling digital communication channels (e.g., WhatsApp, chatbots, automated messaging).
• Monitor performance and quality on digital platforms, ensuring alignment with tone, compliance, and service expectations.
• Collaborate with digital operations and IT/ DevOps teams to improve digital process flows and reporting tools.
• Identify gaps in team capability or process design related to digital communication and escalate to relevant stakeholders.
• Champion a digital-first mindset within the team and promote the use of digital tools to enhance productivity

About You

• Minimum 2–3 years’ experience in a back-office or document processing environment (preferably Finchoice)
• 3 years' experience in team leadership or leadership role or leadership qualification
• Strong leadership and motivational abilities.
• Effective decision-making and problem-solving skills.
• Customer-centric attitude.
• Proactive and innovative mindset.
• Excellent interpersonal and communication skills.
• Ability to work independently and within a team.
• Strong planning and organizational skills.
• Analytical with attention to detail.
• Resilient and adaptable to change.
• Self-driven growth mindset and coaching orientation
• Inspirational leadership through empathy, integrity, and accountability
• Customer obsession and professional representation
• Forward-thinking and innovation mindset
• Results delivery through effective planning and ownership