Data Scientist – Credit Risk Analytics

About the role

The Data Scientist will join GoTyme’s Merchant Cash Advance (MCA) Credit Analytics team to drive credit risk assessment and portfolio optimisation. You will work end‑to‑end, from data exploration and feature engineering to building production‑ready models, monitoring performance and running experiments that improve credit decisioning.

Key responsibilities

• Design, develop and maintain acquisition scorecards and predictive models to evaluate MCA applicants.
• Engineer and iterate credit‑risk features, including behavioural signals and affordability proxies, in close collaboration with senior modelers and engineering.
• Apply modern machine‑learning techniques (ensemble methods, cross‑validation, hyper‑parameter tuning) to enhance model robustness, stability and deployability.
• Monitor provision models to ensure compliance with regulatory and accounting standards such as IFRS 9.
• Build and improve dashboards and monitoring tools that track credit performance, early‑warning signals and model drift.
• Document model governance processes and support model validation activities in a regulated environment.

Required profile

• 3+ years of experience in data science, credit analytics or credit risk management within a bank, fintech, lender or consulting firm.
• Strong background in statistical modelling, machine learning and predictive analytics.
• Ability to communicate technical insights to both technical and non‑technical stakeholders.
• Curious, pragmatic and comfortable iterating quickly while maintaining quality.
• Experience with SME lending, merchant cash advances or alternative credit products is a plus.

Required skills

• Python
• SQL
• Statistical modelling and predictive analytics
• Credit risk modelling (scorecards, provisioning models)
• Machine‑learning libraries and techniques (ensemble methods, cross‑validation, hyper‑parameter tuning)
• Version control (Git/Bitbucket)
• Cloud data platforms (Databricks, BigQuery, Snowflake, AWS, GCP, Azure)