Sr. Applied Scientist - Alexa, Discovery & Engagement

Key Skills

Java Programming, Python Programming, SQL, Machine learning techniques, Data Modeling, Algorithms

Job Description

Help us shape the future of Alexa by helping customers discovery and engage with Alexa's capabilities through programmatic recommendations & personalization. We are looking for a seasoned science leader to work with a team of applied scientists, research scientists, and language engineers to optimize voice and visual experiences across Amazon Echo devices. As part of the Alexa Experience science team, you will be solving hard problems in how Alexa adapts to customer’s latent goals and learns at scale.

You will create personalization, multi-armed bandits, reinforcement learning models, and recommender systems. You will identify new areas of exploration in the recommendations and content optimization space, continuously pushing the bar for what’s possible. You will mentor other applied scientists and deliver innovative and scalable state of the art solutions, with opportunities to publish your work. You will develop novel solutions using large-scale real-world datasets and partner closely with a dedicated engineering and product team to build, evaluate, and deliver your solutions. You will influence the long-term direction of the Alexa voice experience, and be a strategic leader contributing to Alexa’s intelligence, long term product strategies, and business prioritization.

BASIC QUALIFICATIONS

• MS or Ph.D in CS, Machine Learning, Economics, Statistics, or in a highly quantitative field
• 5+ years of experience in applied science / machine learning and software development at large scale
• A deep understanding of machine learning, algorithms and computational complexity.
• Proven ability to relate to and solve business problems through machine learning, economic modeling and statistical algorithms.
• Experience in building systems or applications from the ground-up.
• Ability to coordinate cross functional teams and communicate effectively with both technical and non-technical teams.
• Technical fluency; comfort understanding and discussing architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members.

PREFERRED QUALIFICATIONS

• Ph.D. in Computer Science, Applied Mathematics, Economics or a closely related field.
• Experience in building and scaling personalization and recommender systems.
• Experience in mentoring and developing machine learning engineers and/or research scientists.