Decision Scientist, Decision Sciences

About the job

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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