Credit Manager Jobs

Role purpose

As Data Science Manager, you will combine active, hands-on delivery with a senior technical presence that raises the bar for the team around you. You will work end-to-end on credit risk strategy, features, and analytics, while also acting as a day-to-day technical reference point for junior colleagues based in the same office. You will contribute to the development and performance of the broader local data science team, working closely with data science managers across other locations to ensure the team operates to consistent standards.

Key responsibilities

1. Hands-On Delivery

• 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 — working closely with modellers and engineering.
• Design, run, and evaluate credit policy experiments (cut-offs, limits, pricing/risk trade-offs, segment strategies), including post-implementation reviews.
• Support portfolio analytics: vintage analysis, roll-rates, migration, early warning indicators, collections funnel analytics, and loss driver deep-dives.

2. Technical Leadership & Standards

• Set the standard for analytical rigour, code quality, and documentation within the local team.
• Review the work of junior data scientists, providing structured technical feedback and ensuring output meets the bar required for production or stakeholder use.
• Act as the primary in-office technical reference point — available for pair working, problem-solving, and day-to-day guidance on analytical and modelling questions.
• Identify and address gaps in technical approach before they become delivery risks; escalate where appropriate.

3. People and Collaboration

• Contribute meaningfully to performance reviews and development conversations for data scientists based in the same office, working in partnership with their respective line managers.
• Support onboarding of new team members and help them get up to speed with tooling, data environments, and team ways of working.
• Work closely with data science managers in other locations to maintain consistency of standards, priorities, and delivery practices across the team.

4. Data, Infrastructure & Governance

• Partner with Data and 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, and change logs.

Requirements

Required Experience and Qualifications:

• 8–12 years in credit analytics, credit risk modelling, or lending data science (bank, fintech, lender, bureau, or consulting) 
• Demonstrable experience working as both, a senior individual contributor and a people manager.
• Strong Python and/or SQL skills with experience working with large datasets in production-grade environments.
• Deep grounding in statistics and predictive model evaluation: ranking performance, calibration, stability, and business impact measurement.
• Clear, structured communication skills with both technical peers and non-technical stakeholders.

Nice to Have

• Experience with bureau data, open banking/transactional data, device/behavioural signals, or alternative data sources.
• Familiarity with governance and documentation practices in regulated lending environments.
• Exposure to cloud analytics stacks (e.g., BigQuery, Snowflake, Databricks) and version control (Git).
• Prior experience in a distributed or multi-location team environment.

Personal Attributes

• Technically credible and naturally consultative — someone junior colleagues gravitate toward with questions.
• High personal standards for quality, with the ability and inclination to hold those standards in others without being heavy-handed.
• Self-directed and dependable; able to keep work moving and the team focused without close supervision.
• Collaborative and comfortable working across locations, functions, and time zones.
• Pragmatic about trade-offs between rigour and delivery pace; focused on measurable outcomes.

Reporting Line and Location

• Reports into the Credit Analytics Centre of Excellence.
• Location: Bengaluru, India. The role involves close day-to-day collaboration with the local data science team, as well as ongoing coordination with data science managers and credit stakeholders based in other locations.