Amazon is looking for a motivated individual with strong statistical, analytical skills and technological experience to join the DIGI (Data Infrastructure and Generative Intelligence) Ad Sales Finance analytics team. In this position the successful candidate will be responsible for partnering with Finance and Business leaders to optimize Sales Forecasts.
Key Job Responsibilities
* Build and train models to support forecasting and planning for Advertising Sales Finance
* Have strong technical experience, but also be able to work with non-tech partners and communicate complex and technical topics in a simple and understandable fashion
* Have a good understanding of machine learning or statistical modeling techniques, including a strong understanding of model parameters and how they affect performance
* Understand time-series forecasting techniques (e.g., STL decomposition, ETS/Holt-Winters, ARIMA, Prophet, or similar models)
* Familiarity with hierarchical or segmented forecasting problems (e.g., product, region, channel, or customer-level splits)
* Apply theoretical or statistical models in an applied, real-world environment
* Perform model evaluation such as confidence intervals, error metrics, backtesting, and validation datasets
* Work with large, complex datasets across multiple dimensions
* Translate analytical findings into clear, actionable insights for business stakeholders
A day in the life: In this position the successful candidate will be responsible for partnering with Finance and Business leaders to expand and optimize forecasting models that support weekly, monthly, quarterly and annual reviews for the Display Ads Finance group and our stakeholders.
About the team: The Advertising Sales Finance Analytics & FP&A team's responsibilities comprise corporate reporting, planning, Headcount & OpEx, Goals reporting, and ad‐hoc analysis. We support Advertising leaders and finance teams by coordinating and consolidating deliverables, centralizing and standardizing processes, establishing financial controls and mechanisms, building tools that improve the speed of decision making, and providing insightful financial analysis on the short and long term strategy of Advertising.
Basic Qualifications
* 5+ years of data scientist experience
* 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
* 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
* Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
* Experience applying theoretical models in an applied environment
Preferred Qualifications
* Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
* Knowledge of machine learning concepts and their application to reasoning and problem-solving
* Experience in Python, Perl, or another scripting language
* Experience in a ML or data scientist role with a large technology company
* Experience in defining and creating benchmarks for assessing GenAI model performance
* Experience working on multi‐team, cross‐disciplinary projects
* Experience applying quantitative analysis to solve business problems and making data‐driven business decisions
* Experience effectively communicating complex concepts through written and verbal communication
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.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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