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Amazon Sr. Data Scientist, FireTV Business & Marketing in Sunnyvale, California

Description

The Amazon Fire TV Team is looking for a passionate and solution oriented Data Scientist to help redefine and build new, science-driven experiences for customers enjoying Fire TV across the world.

As a key member of the team, you will provide machine learning expertise that helps accelerate the business. You will build various data and machine learning models that help us innovate different ways to enhance the customer experience. You will need to be entrepreneurial, wear many hats, and work in a highly collaborative environment across engineer, product, marketing and BI. We like to move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality.

An ideal candidate will be an expert in the areas of machine learning and statistics who will have expertise in applying theoretical models in an applied environment. The candidate will be expected to work on numerous aspects of Machine Learning such as feature engineering, predictive modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and reinforcement learning. We deal with HUGE volume of data, including clickstream, Alexa, Prime Video, and retail, so challenges will involve dealing with very large data sets and requirements on throughput.

Responsibilities include:

  • Design, implement, test, deploy, and maintain innovative data and machine learning solutions to accelerate our business.

  • Create experiments and prototype implementations of new learning algorithms and prediction techniques

  • Collaborate with scientists, engineers, product managers, and stockholders to design and implement software solutions for science problems

  • Use machine learning best practices to ensure a high standard of quality for all of the team deliverables

  • Proficiency in model development, model validation and model implementation for large-scale applications

  • Ability to convey mathematical results to non-science stakeholders. Strength in clarifying and formalizing complex problems

  • Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts

We are open to hiring candidates to work out of one of the following locations:

Seattle, WA, USA | Sunnyvale, CA, USA

Basic Qualifications

  • 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

  • 4+ years of data scientist experience

  • Experience with statistical models e.g. multinomial logistic regression

Preferred Qualifications

  • 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience

  • Experience managing data pipelines

  • Experience as a leader and mentor on a data science team

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $127,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.

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