Amazon
Sr. Data Scientist, FireTV Business & Marketing Job ID: 2879767 | Amazon.com Services LLC
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.
All potential applicants are encouraged to scroll through and read the complete job description before applying.
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 engineering, product, marketing and business intelligence. 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 volumes 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 stakeholders 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 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 - Experience with design, develop and deploy large-scale machine learning models; Experience with optimization and personalization related problems. PREFERRED QUALIFICATIONS - Experience managing data pipelines - Experience as a leader and mentor on a data science team - 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience 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.
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All potential applicants are encouraged to scroll through and read the complete job description before applying.
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 engineering, product, marketing and business intelligence. 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 volumes 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 stakeholders 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 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 - Experience with design, develop and deploy large-scale machine learning models; Experience with optimization and personalization related problems. PREFERRED QUALIFICATIONS - Experience managing data pipelines - Experience as a leader and mentor on a data science team - 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience 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.
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