Data Scientist II, Amazon 1P Credito, Payments

AI in this role

Data scientist to design, develop, and deploy machine learning and GenAI solutions for payment and credit operations.

pythonmachine-learning-frameworks

machine-learningdata-sciencepredictive-modelingstatistical-analysis

Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for.
We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems.
This role requires additional exposure and experience to Machine Learning.

Key job responsibilities
• Use machine learning and analytical techniques to create scalable solutions for business problems
• Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes
• Design, development, evaluate and deploy innovative and highly scalable models for predictive learning
• Research and implement novel machine learning and statistical approaches
• Work closely with software engineering teams to drive real-time model implementations and new feature creations
• Work closely with business owners and operations staff to optimize various business operations
• Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
• Mentor other scientists and engineers in the use of ML techniques
• Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions
• Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers
• Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries

About the team
Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.

Basic qualifications

- Experience working as a Data Scientist
- Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Bachelor's degree in Science, Technology, Engineering, or Mathematics (STEM)

Preferred qualifications

- Master's degree in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience working in credit risk domain and building risk, income and/or fraud models.

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

Data Scientist II, Amazon 1P Credito, Payments 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.

Key facts

Data Scientist II, Amazon 1P Credito, Payments is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in BR, SP, Sao Paulo.
The job was posted on October 6, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

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 LearningData SciencePredictive ModelingStatistical AnalysisPythonMachine Learning Frameworks

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 data science was part of your work. What did you do?
Tell me about a project where predictive modeling was part of your work. What did you do?
Tell me about a project where statistical analysis was part of your work. What did you do?
Walk me through how you've used Python in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Machine Learning, Data Science, Predictive Modeling, Statistical Analysis, and Python. 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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