Applied Scientist, Amazon Private Brands

Description

The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon.

Key job responsibilities

• Experience in causal ML and treatment effect estimation, including methods like propensity scoring, doubly robust estimators, and uplift modeling.
• Strong background in Python, ML pipelines, and deploying models to production with robust monitoring and evaluation.
• Familiarity with causal inference frameworks and translating business questions into actionable causal insights.
• Drive applied science projects in machine learning end-to-end: from ideation over prototyping to launch. For example, starting from deep scientific thinking about new ways to support customers’ journeys through discovery, you analyze how customers discover, review and purchase Private Brands to innovate marketing and merchandising strategies.
• Propose viable ideas to advance models and algorithms, with supporting argument, experiment, and eventually preliminary results.
• Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions.
• Present results, reports, and data insights to both technical and business leadership.
• Constructively critique peer research and mentor junior scientists and engineers.
• Innovate and contribute to Amazon’s science community and external research communities.

Basic Qualifications

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development
• Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
• Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.