If you like to own solving end-to-end business problems with machine learning which would have a direct impact on the bottom line of Amazon’s business, if you see how big data and cutting-edge technologies can be used to improve customer experience, if you eager to innovate, to discover knowledge from structured and unstructured data and if you deliver results, then we want you to be in our team.

We are responsible for end to end processing of impressions, views, clicks as well as video interaction events or rich media events in ads.
Our data must always be the fastest, most high-fidelity data as it is both billable, critical to checking the heartbeat of a campaign and changes made to it during its lifecycle.

Role Description
You are an individual with outstanding analytical abilities and comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams.

As a Sr. Machine Learning Scientist, you will be a member of an algorithm team that is responsible for the delivery of ML based algorithms. You will design, perform statistical analysis and deliver ML models.

Major responsibilities
· Use statistic analysis and machine learning techniques to create scalable solutions for business problems
· Analyze and extract relevant information from large amounts of both structured and unstructured data
· Design, experiment and evaluate highly innovative models for for regression and classification challenges
· Maintain, evaluate and improve existing models
· Research and implement novel machine learning and statistical approaches
· Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation together with software engineering teams
· Track general business activity and provide clear, compelling management reporting on a regular basis

BASIC QUALIFICATIONS

· Experience programming in Java, C++, Python or related language
· Master's degree with applied research experience
· Experience of building machine learning models for business application
· A PhD in CS Machine Learning, Statistics, or in a highly quantitative field
· 5+ years of hands-on experience in building machine learning models for business applications
· 3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· Algorithm and model development experience for large-scale applications
· Experience in mentoring junior team members, and guiding them on machine learning and data modeling applications
· Strong communication and data presentation skills
· Strong problem solving ability

PREFERRED QUALIFICATIONS

· A PhD in CS Machine Learning, Statistics, or in a highly quantitative field
· 5+ years of hands-on experience in building machine learning models for business applications
· 3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· Algorithm and model development experience for large-scale applications
· Experience in mentoring junior team members, and guiding them on machine learning and data modeling applications
· Strong communication and data presentation skills
· Strong problem solving and dive deep ability
· Proven record of delivering results
· 1+ year distributed programming experience
· Strong skills with Spark/Java/Python (or similar scripting language)
· Strong Communication, writing and data presentation skills
· Functional knowledge of AWS platforms such as S3, Glue, Athena, SageMaker

Amazon Science (www.amazon.science) gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.