ABOUT THIS ROLE
We are seeking a data scientist builder to join the Audible economics team. Our group of economists, data scientists, and analysts tackles a wide range of questions, including pricing, experimentation science, data-driven product strategy/optimizations, internal productivity/incentives, audience science, and impact/ROI measurement. The ideal candidate will enjoy wearing many hats, possess an economist's mindset and a strong ability to effectively translate business questions into tractable quantitative frameworks, and excel at leveraging AI to build and scale robust, interpretable, and production-ready models/systems/tools. We're looking for someone who automates the repetitive, builds tools that force-multiply the team's output/influence, and treats AI as a core part of their workflow - not a side project. If you are passionate about leveraging data to shape the future of digital media, we encourage you to apply and be a part of our dynamic team.
As a Data Scientist, you will...
- Collaborate with economists, analysts, and other data scientists to build and scale econometric/ML models and quantitative tools - owning end-to-end scoping, data pipelining, feature engineering, model development/refinement, production-grade deployment, impact measurement, and adoption
- Research and evaluate emerging tools and techniques (AI-driven and otherwise), and identify novel data sources to leverage in quantitative work - both from within Audible/Amazon and from 3P sources
- Collaborate closely with Product, Content, and Marketing partners to drive broad impact and ensure that solutions are integrated into cross-functional workflows and executive decision-making
- Represent the team in a range of settings - from reviews with senior Amazon scientists to reviews with senior Audible/Amazon business leaders
- Mentor junior scientists and raise the bar for a new generation of scalable, AI-enabled science/analytical work, both within Audible and across the broader Amazon community
Key job responsibilities
BASIC QUALIFICATIONS
- Master's degree or above in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or PhD
- 2+ years hands-on work experience in an applied/industry setting using Python and SQL to work efficiently at scale with large data sets, constructing empirical analysis, modeling, and compelling data visualizations
- Experience formulating and solving loosely-defined optimization and/or measurement problems
- Familiarity with economic methods/reasoning - causal thinking, incentive design, counterfactual frameworks - and an eagerness to continue to learn new econometric methodologies
- Intellectual curiosity for what drives customer value and business outcomes
- Comfort leading ambiguous projects from zero-to-one
PREFERRED QUALIFICATIONS
- Fluency in mathematical foundations of statistics, machine learning, and economics
- Experience applying econometric methods at scale (structural modeling, observational causal inference techniques, etc.) to inform pricing/sales optimizations, investment decisions, experimentation science, and/or product optimizations in the digital media space
- Experience with model monitoring, debugging, and long-term maintenance in production environments
- Familiarity with financial/accounting metrics/methods
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, NJ, Newark - 175,100.00 - 236,900.00 USD annually
