Decision Scientist, Decision Sciences

AI in this role

Build statistical and machine learning models, design A/B tests, and drive data-informed decision making for the 1P Lending team.

pythonsqlr

machine-learningstatistical-modelinga-b-testing

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)

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Decision Scientist, Decision Sciences at Amazon rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Machine LearningStatistical ModelingA B TestingPythonSQLR

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where statistical modeling was part of your work. What did you do?
Tell me about a project where a b testing was part of your work. What did you do?
What's a project where you used Python hands-on?
Walk me through how you've used SQL in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Machine Learning, Statistical Modeling, A B Testing, Python, and SQL. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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