Data Scientist II, Amazon 1P Credito, Payments

Amazon Science; São Paulo; Presencial; BRL 160.000 - 220.000; Jornada completa

Descripción de la vacante

Amazon Science in Brazil's Payments team seeks a data scientist to unlock business insights from large datasets and shape analytical infrastructure for strategic decisions. The role focuses on building scalable ML solutions that can scale across millions of customers in multiple countries.

You will design and deploy end-to-end ML models in production, collaborate with engineering and operations teams, and mentor colleagues to advance ML capabilities within the organization.

Formación

• Experience working as a Data Scientist.
• Experience with data scripting languages (SQL, Python, R) or statistical software.
• Bachelor's degree in Science, Technology, Engineering, or Mathematics (STEM).

Responsabilidades

• 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

Conocimientos

Data Scientist experience

ML/statistical modeling

Educación

Bachelor's degree in STEM

Herramientas

SQL

Python

R

Descripción del empleo

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.

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.

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