Tyme

Machine Learning Engineer

• 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).

Scorecard Developer – Machine Learning Specialist

Data Scientist

• Analyse customer, bureau, transactional and repayment data to identify drivers of risk, loss, approval rates and customer outcomes.
• Build and iterate credit risk features and model inputs (behavioural signals, affordability proxies, stability-tested transformations), partnering closely with senior modellers and engineering.
• Contribute to development and improvement of predictive models using modern machine learning approaches, with a focus on robustness, stability and deployability.
• Design, run and evaluate credit policy experiments (cut-offs, limits, pricing/risk trade-offs, segment strategies), including post-implementation reviews.
• Develop monitoring for model/policy performance and feature health (drift, stability, segment performance, data quality checks).
• Support portfolio analytics: vintage analysis, roll-rates, migration, early warning indicators, collections funnel analytics, and loss driver deep-dives.
• Work with Data/Engineering to improve data definitions, quality, lineage and reproducible pipelines; document feature logic and assumptions.
• Contribute to governance documentation (model inputs, feature catalogues, monitoring evidence, change logs).

Credit Data Scientist – Credit Analytics

Data Engineer

• Design, develop, test, deploy and monitor data pipelines in Databricks on AWS from a wide variety of data sources.
• Design, develop, test, deploy and monitor scalable code with PySpark and SQL in Databricks.
• Identify opportunities to improve internal process through code optimisation and automation.
• Build data quality dashboards, lineage flows / and or monitoring tools to utilize the data pipeline, providing active monitoring and actionable insight into overall data quality and data governance.
• Assist in migrating data from legacy systems onto newly developed solutions.
• Follow and lead best practices on all data security, retention, and privacy policies.

Data Engineer – Lending

QA Engineer

• Manage and drive QA excellence in the scrum teams.
• Work closely with other QAs, engineers, analysts and product owners to identify testing areas across deliverables.
• Oversee the creation and execution of test plans and testing strategy for the scrum teams.
• Liaise with Engineering managers and leads for overall end-to-end testing plan and ensure quality releases from Non-Prod environments to Production.
• Lead the automation testing efforts and liaise with other QA leads for reusable QA frameworks.
• Communicate status, risks and issues to senior management and team members.
• Drive QA process and standards changes for high-performing teams.
• Recruiting, training and coaching QA engineers.
• People management for up to 10 QA engineers.
• Build quality culture for both QA team and feature product team.
• Accountable for industry testing, collaborating with QA team in local bank and 3rd party.

Scorecard Developer – Machine Learning Specialist

Credit Data Scientist – Credit Analytics

Data Engineer – Lending

Quality Assurance Lead – QA QC, Tester, English