Credit Data Scientist (Credit Analytics) - Bengaluru

GoTymeX; Bengaluru; On-site; INR 1,200,000 - 1,600,000; Full time

Job summary

GoTymeX in Bengaluru, India seeks a Credit Data Scientist to advance credit decisioning and portfolio performance across lending products and markets. You will explore data, engineer features, and run experiments from data discovery to production-aligned features and monitoring.

You will build risk signals, monitor model and policy performance, collaborate with data engineering, adhere to governance, and communicate findings to both technical and non-technical stakeholders.

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.

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).

Skills

Credit analytics

Python

SQL

Statistics

Machine learning

Communication

Tools

BigQuery

Snowflake

Databricks

Git

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).

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

• 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.

Reporting line and location

Reports into credit analytics center of excelence. Location: Bengaluru, India. With collaboration with in-country lending and credit risk teams.