GoTyme MCA (SA): The Data Scientist will play a pivotal role in assessing, analyzing, and mitigating credit risk within the GoTyme MCA (SA) Credit Analytics team. Working end-to-end—from data exploration and feature engineering to production-ready models, monitoring, and experimentation—the role leverages data-driven insights to enhance credit decisioning, optimize portfolio performance, and support the continued growth of the Merchant Cash Advance (MCA) product.

Requirements

Required Competencies and Skills

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.

Qualifications

Degree in Data Science, Statistics, Mathematics, or a related quantitative field.
Professional Qualification and/or Regulatory, Licensing requirements (if any)
None mandated, though familiarity with SARB credit risk guidelines and IFRS 9 is advantageous.
Relevant Work Experience
3+ years of experience in data science, credit analytics, or credit risk management within a bank, fintech, lender, or consulting environment.

Key Responsibilities

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.