Data Scientist II, Selection Growth Science

02 ott - Asti
Amazon

Data Scientist II, Selection Growth Science

Prima di candidarsi per questo ruolo, la preghiamo di leggere le seguenti informazioni su questa possibilità, riportate di seguito.
Join us in the evolution of Amazon's Seller business The Selling Partner Selection Succ organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources.

Key job responsibilities

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- Identify opportunities to improve SP growth and translate those opportunities into science problems via principled GenAI solutions.
- Design and execute roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers.




- Work with our engineering partners and draw upon your experience to meet latency and other system constraints.
- Be responsible for communicating our science innovations to the broader internal & external scientific community.

Basic Qualifications

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- 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
- 2+ years of data scientist 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

Preferred Qualifications

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- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience in a ML or data scientist role with a large technology company
- Demonstrated experience leveraging generative AI tools to enhance workflow eff

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