Data Scientist III, Amazon 1P Credito, Payments

Description

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 ml models such as risk scorecards, income models, fraud models for predictive learning in credit risk applications
• 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
  
  

Understand the credit risk data and evaluate the best ml model/ solution for dynamic business problems.

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 with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
• Experience as a leader and mentor on a data science team
• Experience with statistical models e.g. multinomial logistic regression
• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
  
  

Preferred Qualifications

• Experience documenting modeling for technical and business leaders
• Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
• Experience with data visualization using Tableau, Quicksight, or similar tools
• Experience working with scientists, economists, software developers, or product managers
• 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.