Junior AI/ML Engineer supporting data preprocessing and model evaluation at Node.Digital. Collaborate on AI/Machine Learning efforts with government and commercial clients.
About the role
- Scorecard Developer responsible for developing and maintaining credit scoring components and calibrations. Collaborating with engineering teams to deliver high-quality scoring solutions for credit decisioning.
Responsibilities
- Develop and maintain scoring solutions and supporting artefacts used in credit decisioning (application and/or behavioural scoring, segmentation, risk signals).
- Own feature engineering for scoring: create, test and document variables from bureau, application, transactional and repayment data; ensure stability, interpretability and data quality.
- Contribute to model development and tuning using modern machine learning approaches where appropriate, ensuring outputs are robust, stable and suitable for decisioning.
- Apply best-in-class machine learning practices for credit scoring, including disciplined hyperparameter optimisation, robust validation, and repeatable model selection workflows appropriate for production decisioning.
- Define and maintain feature specifications for production (definitions, transformations, edge-case handling, missing value logic, consistency checks).
- Produce PD / score calibrations to observed bad rates (overall and by segment), including calibration curves, stability tracking, and recalibration recommendations.
- Support cut-off / limit strategy analysis using calibrated risk outputs (approval rate vs bad rate vs loss trade-offs).
- Run ongoing monitoring: drift and stability of inputs/features, score distribution shifts, performance by segment and cohort/vintage, data pipeline health.
- Partner with Engineering / Decisioning teams to operationalise scoring outputs and ensure reproducibility (versioning, back-testing, change control).
- Maintain clear documentation suitable for internal review/audit (feature catalogue, calibration approach, monitoring packs, change logs).
Requirements
- 2–4 years’ experience in credit scoring / risk modelling / decisioning analytics in a lender, bank, bureau, or fintech setting.
- Strong SQL plus Python/R for feature engineering, analysis, monitoring and calibration work.
- Practical experience with advanced machine learning concepts (e.g., ensemble methods, feature selection, hyperparameter tuning, cross-validation) and the discipline to balance predictive power with stability and governance needs.
- Experience translating model outputs into business-ready risk measures via calibration and performance tracking.
- Ability to produce implementation-ready specifications and work closely with engineering/decisioning stakeholders.
- Exposure to multi-country portfolios and different bureau ecosystems.
- Familiarity with model risk governance, validation support, and evidence pack preparation.
- Experience with real-time/batch scoring pipelines and feature stores.
- Detail-oriented and quality-driven; enjoys building reliable, production-ready data logic.
- Practical communicator who can translate analytics into deployable specs and monitoring.
- Comfortable operating across analytics + implementation + monitoring.
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