# Data Scientist

**Company:** [GoTyme ZA (South Africa)](http://jobs.workable.com/companies/i6AcWcWsEVH44qNppMYfw9.md)
**Location:** Cape Town, South Africa
**Workplace:** hybrid
**Department:** South Africa - Banking Services

[Apply for this job](http://jobs.workable.com/view/7dc1c466-f817-4ad2-ac93-e5bce54e889b)

## Description

**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.