Applied Scientist

Job description

DESCRIPTION

We are looking for an Applied Scientist to join the Devices Demand Planning team that manages forecasting and optimization for the entire Amazon device family of products and accessories. Predicting the sales volume for every Amazon device and accessory for every day of the year 6 to 12 months in advance requires the ability to develop and use scalable and robust state-of-the-art algorithms that involve learning from large amounts of data, such as customer engagement data (impression, clicks, transactions, etc.), product features (product attributes, price, promotion, etc.), merchandising activities, relevant products and users, in order to drive more efficient customer engagement and business values.

As our organization continues to grow, our new ML platforms are being put into place to make discovery, prototyping and delivery of new algorithms and features faster in an environment that requires rapid innovation and expertise. As a member of the DIAL team you will pave the way for other Scientists to deliver better solutions faster. As a leader in the organization, you will be setting standards, paving new pathways for Amazon and extending influence across multiple teams.

This role is central to the continued growth of the Amazon Device division as we continue to drive sales, reduce costs and become more central as Amazon continues to find ways to increase profits, reduce waste, and maintain our position as on the leading edge of technology. We have grown from only supporting the first Kindle e-reader to a vast portfolio of Fire tablets, Fire TVs, Echo, and Dash buttons. You will have an opportunity to both develop advanced scientific solutions, drive critical customer and business impacts, and work closely with the development team to pave the way for our team, and all of Amazon. You will play a key role to drive end-to-end solutions from understanding our business and business requirements, identifying opportunities from a large amount of historical data, building prototypes and exploring conceptually new solutions, running online experiments, to working with partner teams for prod deployment. You will collaborate closely with engineering peers as well as business stakeholders. You will be at the heart of a growing and exciting focus area for Amazon Devices.

You are an individual with outstanding analytical abilities, excellent communication skills, and are comfortable working with cross-functional teams and systems. You will be responsible for researching, prototyping, experimenting, and analyzing predictive and optimization models.

Key responsibilities:
· Drive scalable solutions from the business, to prototyping, production testing and through engineering directly to production.
· Process and analyze sales, website, and other user data to gather additional data sources that would improve model performance. Find creative ways to reduce MAPE, etc.
· Prototype models by using high-level modeling languages such as R or in software languages such as Python.
· Create and track accuracy and performance metrics (both technical and business metrics)
· Create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders.
· Drive best practices on the team; mentor and guide junior members to achieve their career growth potential

This is an opportunity to become a founding member of a brand-new team of Machine Learning Services in Amazon Devices. One of our exciting projects is to drive Echo Creative Recommendation on Amazon Gateway page by developing personalized ranking and recommendation systems with advanced machine learning techniques.

Desired profile

BASIC QUALIFICATIONS

· Master's or PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
· 3+ years of practical experience applying ML to solve complex problems
· Algorithm and model development experience for large-scale applications
· Experience using Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language
· Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives