Level

Amazon

Data Scientist I, Amazon 1P Credito, Payments

AI in this role

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 with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience as a data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Bachelor's degree or above in Math, Statistics, Computer Science, or related Science field

Preferred qualifications

- Master's degree in Science, Technology, Engineering, or Mathematics (STEM)
- Experience working effectively with science, data processing, and software engineering teams

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 I, Amazon 1P Credito, Payments at Amazon rates 84 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.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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.

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