Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon 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.
Please visit https://www.amazon.science for more information.
This role requires working closely with business, engineering and other scientists within RME and across Amazon to deliver ground breaking features. You will lead high visibility and high impact programs collaborating with various teams. You will work with a team of Data Scientists and Research Scientists to build solutions for Amazon.
The ideal candidate will be an expert in the areas of data science, machine learning and statistics, having hands-on experience with multiple improvement initiatives as well as balancing technical and business judgment to make the right decisions about technology, models and methodologies.
Key responsibilities of a Data Scientist in Amazon RME include:
· ·Working with technical and non-technical customers to design in-production experiments and simulations and communicate results
· Working alongside Research Scientists developing new techniques in sampling, simulation, experimental design and analysis to capture new use cases and improve fidelity
· Collaborating with our dedicated software team to create production implementations for large-scale data analysis
· Developing and owning key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business
· Deep dives on experimental and simulation results to yield additional insights and guidance for future experiments and simulations
· Creation and maintenance of dynamic automated reports that leverage visualization to aid critical decision making
· Driving training sessions with internal customers to facilitate efficient usage of experimentation and standardize results
· Master’s degree with 3 years relevant experience in a highly quantitative field (Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.), or equivalent professional
· Hands-on industry experience in predictive modeling and analysis, as an ML engineer or data scientist , applying various ML techniques, and understanding the key parameters that affect their performance.
· Proficient with using scripting languages such as Python
· Experienced in using multiple data science methodologies to solve complex business problems (e.g. statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, demand modeling, etc.).
· Experienced in handling terabyte-sized data sets, diving into data to discover hidden patterns, using data visualization tools, using SQL and databases in a business environment.
· Ability to distill informal customer requirements into problem definitions.
· Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge, educating them about our systems, as well as sharing insights and data-driven recommendations.
· 5+ years’ experience in a ML or Data Scientist role with a large technology company.
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, NLP, deep learning, recommendation systems, dialogue systems, information retrieval, ·
· Skilled with Java, C++, or other programming language, as well as Python or similar scripting language.
· Functional knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker.
· Experience taking a leading role in building complex data based products that have been successfully delivered to customers.
· Demonstrated industry leadership in the fields of data science and big data processing.
· Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations