Data Scientist: Long-Term Demand Forecasting & Strategy

Amazon; Seattle (WA); On-site; USD 136,000 - 184,000; Full time

Job summary

Amazon is seeking a Data Scientist II on the Devices forecasting team to build econometric and machine learning models that project long-term demand and quantify willingness to pay for features. You will translate results into product strategy alongside product managers, engineers, and stakeholders.

You will mentor junior data scientists, present findings with clear visuals, and help shape roadmap through rigorous analysis and scalable methods.

Qualifications

• 3+ years of data querying languages (e.g. SQL) or statistical/mathematical software experience.
• 2+ years of data scientist experience.
• 3+ years of ML/statistical modeling data analysis tools and techniques experience.
• Bachelor's degree.
• Experience applying theoretical models in an applied environment.

Responsibilities

• Build and validate econometric and machine learning models that generate long-term demand forecasts for Amazon Devices.
• Assess the incrementality of new products and quantify willingness to pay for product features.
• Collaborate with product managers, engineers, and business stakeholders to scope analytical projects, define metrics, and identify data requirements.
• Communicate findings to technical and non-technical audiences through clear documentation, visualizations, and presentations.
• Mentor less experienced data scientists through code reviews, knowledge sharing, and team planning.

Skills

SQL

Python

R/SAS/Matlab

Education

Bachelor's degree

Tools

Matlab

SAS

Job description

Amazon is seeking a Data Scientist II on the Devices forecasting team to build econometric and machine learning models that project long-term demand and quantify willingness to pay for features. You will translate results into product strategy alongside product managers, engineers, and stakeholders.

You will mentor junior data scientists, present findings with clear visuals, and help shape roadmap through rigorous analysis and scalable methods.