Associate Data Science R Jobs in Seattle, WA (NOW HIRING)

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
- Experience in a least one area of Machine Learning (NLP, Regression, Classification, Clustering, or Anomaly Detection)
- Experience with fairness in machine learning and artificial intelligence to detect and remove bias in ML/AI systems
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 175,100.00 - 236,900.00 USD annually