Manager, Data Science, Outbound Communications

Description

• Are you passionate about using data science and machine learning to optimize how hundreds of millions of customers experience communications from the world's most customer-centric company? Join the Outbound

Description

• Are you passionate about using data science and machine learning to optimize how hundreds of millions of customers experience communications from the world's most customer-centric company? Join the Outbound Communications Intelligence team at Amazon, where you will lead the development of scalable/robust advanced AI based methods like LLMs and RL to personalize the relevance, frequency and timing of messages across push, email, WhatsApp, and SMS channels reaching 250M+ global customers every week. You will lead the insights arm to build highly accurate and world-class self-service analytics solutions that guide the short- and long-term investments for the business. Key job responsibilities You will lead applied scientists, data scientists and business intelligence engineers to: - Optimize Outbound's inbox management and planning system to personalize frequency, send-time and relevance bar of our messages to customers. - Design and execute large-scale experiments such as multi-arm elasticity tests or RCTs to measure and improve incrementality/performance of our models. - Drive development of HVA propensity models (opt-out, purchase, etc.) to drive intended behavior of customers to their next stage of shopping and engagement with Amazon. - Drive AI-based transformation in data accuracy and reporting: migrating and enhancing the self-serve analytics capabilities developed by the team, automating WBR preparation, building anomaly detection, etc. - Own financial planning frameworks for outbound performance including QxG/HVE forecasting and ROI measurement for paid channel investments. In addition, you will: - Hire, develop, and mentor scientists and BIEs while partnering cross-functionally with engineering, product, marketing, and partner science teams (CBA, P13N, CFV) to productionize solutions at scale. - Create, align and evolve your team's roadmap by prioritizing across multiple competing priorities using high judgement decisions.

Basic Qualifications

• - 5+ years of building quantitative solutions as a scientist or science manager experience - 2+ years of scientists or machine learning engineers management experience - 5+ years of applying statistical models for large-scale application and building automated analytical systems experience - Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field - Knowledge of Python or R or other scripting language

Preferred Qualifications