Applied Scientist, Amazon

• Experience working with technical and non-technical business owners/partners to overcome obstacles and deliver results
• A degree in a quantitative field (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar) with: 5+ years of hands-on industry experience with applied ML (Bachelor's), or 3+ years of applied ML experience (Master's), or 1+ year of applied ML experience (PhD)
• Experience with LLM model evaluations
• Experience owning projects that involve numerous scientists and engineers
• Expertise with: Python and SQL, modern open source ML libraries (Pytorch, Tensorflow, etc.). Developing software in AWS or similar cloud-computing services

Desirable

• We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of Creators and content contextual signals. As an Applied Scientist on Twitch's GTM Science team, you will use applied machine learning to tackle ambiguous business problems across problem areas such as Ads, Brand Safety, Creator Sponsorship and Content Understanding
• You will lead high profile projects where you will use a wide toolbox of ML tools to handle multiple types of data, including user behavior, metadata, and user generated content such as text and video. You will collaborate with a team of passionate scientists, engineers, and product stakeholders to develop these models, prototype and put them into production, where they can help Twitch's creators and viewers succeed and build communities
• You will report to our Senior Data Science Manager for GTM
• Build machine learning products to enrich Twitch’s content understanding and help creators and viewers build and discover their communities
• Propose, design, and oversee execution for high-impact projects that work backwards from customer problems to develop the right solution for the job. Know when to use a classical ML model versus a state-of-the-art one
• Collaborate with cross-functional engineering and product teams to prototype and deploy your models into flexible data pipelines and ML-based services
• Bring innovation and help drive science culture on the team by staying up to date and experimenting with new techniques in LLM, computer vision, inference and model evaluation