Science & Research Jobs in Cape Town, South Africa (Now Hiring) — 25 open

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.

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.