Tyme

Credit Data Scientist – Credit Analytics

Tyme

full-time

Posted on:

Location Type: Remote

Location: India

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Tech Stack

BigQueryCloudPythonSQL

About the role

  • 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

  • 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).
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
credit analyticscredit risklending data sciencePythonSQLRstatisticspredictive model evaluationmachine learningmodel monitoring
Soft Skills
clear communicationcollaborationanalytical thinkingproblem-solving