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Open nowPosted 8 hours ago

Data Scientist (Credit Analytics)

GoTyme ZA (South Africa)

Where
Johannesburg, Gauteng, South Africa
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Your applicationOpen nowData Scientist (Credit Analytics)GoTyme ZA (South Africa) · Johannesburg, Gauteng, South Africa

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  5. 36.6%30 days
  6. 58.9%60 days
This job: posted 8 hours ago

The posting

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: Comprehensive Group Life Cover

  • Life Cover
  • Funeral Cover
  • Global Education Protector
  • Disability Cover (incl. Contribution Protector)
  • Severe Illness Benefit

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.

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.
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