Credit Data Scientist (Credit Analytics)

Actively Reviewing the Applications

TymeX

On-site

Posted 2 days ago • Apply by June 16, 2026

Job Description

Role purpose

As a Credit Data Scientist, you'll use data, feature engineering and experimentation to improve credit decisioning and portfolio performance across our lending products and markets. You'll work end-to-end from data exploration through to production-aligned features, monitoring and impact measurement.

Key Responsibilities

Analyse customer, bureau, transactional and repayment data to identify drivers of risk, loss, approval rates and customer outcomes
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 development and improvement of predictive models using modern machine learning approaches, with a focus on robustness, stability and deployability
Design, run and evaluate credit policy experiments (cut-offs, limits, pricing/risk trade-offs, segment strategies), including post-implementation reviews
Develop monitoring for model/policy performance and feature health (drift, stability, segment performance, data quality checks)
Support portfolio analytics: vintage analysis, roll-rates, migration, early warning indicators, collections funnel analytics, and loss driver deep-dives
Work with Data/Engineering to improve data definitions, quality, lineage and reproducible pipelines; document feature logic and assumptions
Contribute to governance documentation (model inputs, feature catalogues, monitoring evidence, change logs)

Requirements

Required experience and qualifications

2-4 years in credit analytics / credit risk / lending data science (bank, fintech, lender, bureau, consulting)
Strong Python and/or SQL skills and experience working with large datasets
Proficiency in Python or R for analysis and modelling
Solid grounding in statistics and predictive model evaluation (ranking performance, calibration, stability) and business impact measurement
Exposure to advanced machine learning concepts (e.g., ensemble methods, cross-validation, hyperparameter tuning) and an understanding of how to apply them responsibly in production settings
Clear communication skills with technical and non-technical stakeholders

Nice to have

Experience with bureau data, open banking/transactional data, device/behavioural signals, or alternative data
Familiarity with model monitoring, governance, and documentation practices in regulated environments
Exposure to cloud analytics stacks (e.g., BigQuery/Snowflake/Databricks) and version control (Git based)

Personal attributes