# Data Scientist (Credit Analytics) > GoTyme ZA (South Africa) · Cape Town, South Africa (Hybrid) · — · Posted 2026-11-06 **Workplace:** hybrid **Department:** South Africa - Banking Services ## Description The Data Scientist will play a pivotal role in assessing, analysing, and mitigating credit risks within the MCA SA Credit Analytics team at GoTyme. Working end-to-end from data exploration through to production-aligned features and monitoring, the candidate will use data, feature engineering, and experimentation to improve credit decisioning and portfolio performance for the Merchant Cash Advance product. **Credit Risk Modelling** - Develop, implement, and maintain acquisition scorecards and models to evaluate MCA applicants. - 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 the development and improvement of predictive models using modern machine learning approaches, with a focus on robustness, stability, and deployability. - Monitor provision models aligned with regulatory and accounting standards. - Enhance portfolio monitoring tools and dashboards to track credit performance and early warning signals, including drift, stability, segment performance, and data quality checks. **Data Analysis & Insights** - Analyse customer, transactional, repayment, and business health data to identify drivers of risk, loss, approval rates, and customer outcomes. - Identify trends, correlations, and anomalies that impact take up rate, credit performance and portfolio stability. - Support portfolio analytics: vintage analysis, roll-rates, migration, early warning indicators, collections funnel analytics, and loss driver deep-dives. - Collaborate with product, finance, and operations teams to embed data-driven decision-making. **Credit Policy & Experimentation** - Design, run, and evaluate credit policy experiments (cut-offs, limits, pricing/risk trade-offs, segment strategies), including post-implementation reviews. - Develop segmentation and behavioural models to drive proactive portfolio management. - Support stress testing and scenario analysis. **Innovation & Automation** - Design and deploy machine learning models for predictive credit risk assessment. - Leverage advanced analytics to streamline underwriting and risk monitoring processes. - Continuously explore new data sources and analytical methods to improve risk evaluation. - Work with Data/Engineering to improve data definitions, quality, lineage, and reproducible pipelines; document feature logic and assumptions. **Governance & Documentation** - Contribute to governance documentation including model inputs, feature catalogues, monitoring evidence, and change logs. - Ensure all modelling work meets internal standards and applicable regulatory requirements. ## Requirements - Degree in Data Science, Statistics, Mathematics, or a related quantitative field - 3+ years of experience in data science, credit analytics, or credit risk management within a bank, fintech, lender, or consulting environment **Essential** - Strong background in statistical modelling, machine learning, and predictive analytics. - Proficiency in Python and/or SQL. - Experience building and validating credit risk models, including scorecards and provisioning models. - Solid grounding in predictive model evaluation — ranking performance, calibration, and stability — and business impact measurement. - Exposure to advanced machine learning concepts (ensemble methods, cross-validation, hyperparameter tuning) and the ability to apply them responsibly in production settings. - Strong business acumen with the ability to communicate insights to both technical and non-technical stakeholders. - Curious and pragmatic, focused on measurable outcomes; comfortable working in detail and iterating quickly while maintaining quality. - Collaborative and able to work across markets and time zones. **Desirable** - Experience in SME lending, merchant cash advances, or alternative credit products. - Familiarity with IFRS 9, Basel, or equivalent credit risk regulatory frameworks. - Experience with bureau data, open banking/transactional data, device/behavioural signals, or alternative data sources. - Exposure to cloud-based data platforms (Databricks, BigQuery, Snowflake, AWS, GCP, or Azure) and version control (Git/Bitbucket). - Familiarity with model monitoring, governance, and documentation practices in regulated environments. ## Benefits **Our Benefits Include:** ![:light\_blue\_heart:](https://a.slack-edge.com/production-standard-emoji-assets/16.0/google-medium/1fa75@2x.png)**Comprehensive Group Life Cover** - Life Cover - Funeral Cover - Global Education Protector - Disability Cover (incl. Contribution Protector) - Severe Illness Benefit ![:hospital:](https://a.slack-edge.com/production-standard-emoji-assets/16.0/google-medium/1f3e5@2x.png)**Healthcare That Works for You** - Full company contribution to **Health Insurance** for eligible employees. - Partial company contribution to **Medical Aid** (Key Health or Discovery) options for eligible employees. ![:moneybag:](https://a.slack-edge.com/production-standard-emoji-assets/16.0/google-medium/1f4b0@2x.png)**Plan for Your Future** - Access to a **Retirement Annuity**, with convenient payroll deductions to help you save for retirement while benefiting from applicable tax advantages. _Other benefits include:_ - EAP support (counselling, financial and legal for you and anyone financially dependent on you). - Competitive, industry-benchmarked salaries. - Non-guaranteed performance bonuses. - Access to company shares (T's & C's apply). - Wellness days. - Social clubs. - On-site barista. ## Apply [Apply at GoTyme ZA (South Africa)](https://apply.workable.com/gotyme-za-south-africa/j/55B862FF10/apply) --- Powered by [Workable](https://www.workable.com)