Decision Scientist, Decision Sciences

• Amazon is looking for a Decision Scientist I to join 1P Lending team and help drive data-informed business decisions.
• In this role, you will apply statistical modeling, machine learning, and experimentation techniques to solve well-defined problems across key business areas. You will build and validate models using standard methodologies (logistic regression, gradient boosting, survival analysis, classification), design and analyze A/B tests, and create metrics to quantify business impact. Working with large-scale datasets from multiple sources, you will partner with product, business, and engineering teams to translate data-driven insights into actionable strategies.

You will write mathematically rigorous documentation, follow best practices in model development, and deliver artifacts that directly improve business processes and customer outcomes.

• Key job responsibilities
• 1. Build and validate statistical and machine learning models using standard methodologies (logistic regression, decision trees, random forests, gradient boosting, survival analysis).
• 2. Develop classification, regression, and segmentation models to support business decisions
• Monitor deployed models for drift and degradation, and recommend recalibration when needed
• Design, execute, and analyze A/B tests and controlled experiments to evaluate product and policy changes.
• 3. Drive end-to-end delivery of scalable data pipelines from ideation to production deployment.
• 4. Gather and use large-scale datasets from multiple sources to build analytical solutions.
• 5. Write production-quality SQL and Python/R scripts to extract, transform, and analyze data at scale.
• 6. Create and maintain metrics and KPIs to quantify model performance and business improvement (e.g., AUC-ROC, precision-recall, Gini, KS statistic).
• 7. Partner with product, business, science, and engineering teams to translate complex analyses into actionable recommendations.
• 8. Write accurate, clear, and mathematically rigorous technical documents, model documentation, and reports.
• 9. Conduct root cause analysis on business performance trends and anomalies.
• 10. Deliver artifacts for project components that improve processes or systems to inform business decisions.
• 11. Follow best practices to discover and adapt existing knowledge (internal and external research) to meet customer needs.
• 12. Understand what the team owns, including data pipelines, model infrastructure, and business impact.

• 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

• - 3+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
• - 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• - Knowledge of machine learning concepts and their application to reasoning and problem-solving
• - 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
• - Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Preferred qualifications

• - Experience applying quantitative analysis to solve business problems and making data-driven business decisions
• - Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
• - Experience effectively communicating complex concepts through written and verbal communication
• - Experience with AWS Solutions, including EC2, S3, Redshift, EMR (or Hadoop)

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