Amazon

Data Scientist II, International Emerging Stores Payments (IESP) Science Team

Job Description

Do you want to use your expertise in translating innovative science into impactful products to develop a new business line in International emerging stores. If you do, International Emerging Stores Payments team would love to talk to you about how to make that a reality.

As a Data scientist on our team, you will work with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables and define the execution roadmap. You will partner with scientists, and engineers on the design, development, testing, and deployment of scalable ML models.

This is a unique, high visibility opportunity for someone who wants to have impact, dive deep into large-scale solutions, enable measurable actions on the employee experience, and work closely with scientists and economists. This role combines science leadership and technical strength.

Key job responsibilities
- As an Data Scientist, ML Applications, you will:
- Lead Data scientist to deliver machine-learning and AI solutions to production.
- Design, develop, and evaluate innovative machine learning solutions to solve diverse challenges and opportunities for Amazon customers
- Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.
- Partner with the engineering team to deploy your models in production.
- Partner with scientists from across ML teams within India Consumer Payments to solve complex problems.
- Work directly with Amazonians from across the company to understand their business problems and help define and implement scalable ML solutions to solve them.
- Mentor and develop junior scientists and developers.- 2+ years of data scientist 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
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment


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