Senior Decision Scientist, Decision Sciences (Consumer Payments)

Amazon; Pune District; On-site; INR 3,800,000 - 6,000,000; Full time

Job summary

Amazon in Pune, India seeks a Senior Decision Scientist to build and deploy ML models for credit risk, income estimation, fraud detection, and portfolio optimization. You will work with large heterogeneous data sources to inform lending decisions and customer outcomes.

You will collaborate with risk, product, and collections teams, drive end-to-end data pipelines, and mentor junior team members while ensuring governance and regulatory compliance.

Qualifications

• 5+ years of data scientist experience
• 5+ years of experience with SQL and Python or equivalent
• Master's degree in STEM or related field
• Experience applying theoretical models in practice
• Knowledge of machine learning concepts and their application

Responsibilities

• Understand lending business, data, regulatory context and DS practices
• Interact with risk, product and collections teams to define problems
• Design and implement ML solutions for credit lifecycle stages
• Create model validation, monitoring and governance aligned with regs
• Deploy ML solutions in production with sub-second latency
• Develop new modeling techniques for innovative lending products
• Lead end-to-end data pipelines from ideation to prod
• Improve performance and accuracy of ML models
• Derive actionable insights from diverse datasets
• Mentor junior data scientists and present findings to leadership

Skills

Data science experience

SQL

Python

ML concepts

Education

Master's degree in STEM

Tools

AWS (S3/Redshift/Sagemaker)

Hadoop/Spark/Hive

Job description

Senior Decision Scientist, Decision Sciences

We are seeking a talented and motivated Data Scientist to join our 1P Lending team. In this role, you will design, build, and deploy machine learning models and analytical frameworks that drive credit risk assessment, income estimation, fraud detection, and portfolio optimization. You will work with credit bureau data, transactional data, and alternative behavioral signals to create models that directly impact lending decisions and customer outcomes.

Key job responsibilities

• Understand lending business, products, data consumed to make decisions, regulatory context, and practice of Data Science related to this field.
• Closely Interact with risk, product and collection team to understand the business challenges.
• Define, Design and Implement complex ML solutions for various credit lifecycle stages like credit risk scoring, income prediction, collection optimisation, sales optimisation, fraud detection etc.
• Create robust model validation, monitoring, and governance solutions in compliance with regulatory requirements.
• Help the Decision-Engine team to deploy the ML solutions in production with sub-second latency.
• Develop new modeling techniques and analytical frameworks for innovative lending products within the regulatory landscape.
• Drive end-to-end delivery of scalable data pipelines from ideation to production deployment.
• Optimize performance and accuracy of existing ML solutions.
• Derive actionable insights from massive and diverse datasets.
• Lead analytical design and implementation for new global market geographies.
• Mentor junior team members in data science best practices.
• Present findings and recommendations to senior leadership with clear, data-backed conclusions.

About the team

As Decision Sciences team supporting 1p lending, we use Data Science, Machine Learning, and advanced analytics on massive datasets to power credit decisioning, risk management, and customer experience optimization. We operate at the intersection of ML, Fintech and E-commerce, working on cloud scale ML infrastructure. If explainable GenAI, Probabilistic Graph Models, DNNs and Monte Carlo Simulations excite you, then we are the right team for you.

Basic Qualifications

• 5+ years of data scientist experience
• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
• Experience applying theoretical models in an applied environment
• Knowledge of machine learning concepts and their application to reasoning and problem-solving

Preferred Qualifications

• Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
• Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
• Experience applying quantitative analysis to solve business problems and making data-driven business decisions
• Experience working on multi-team, cross-disciplinary projects
• Experience in defining and creating benchmarks for assessing GenAI model performance
• Experience effectively communicating complex concepts through written and verbal communication

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