Data Scientist, LEO Terminals—Predictive Analytics

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

Benefits offered by this job

Health insurance

RSUs

Sign-on payments

401(k) matching

Job summary

Amazon Kuiper Manufacturing Enterprises LLC is seeking a Data Scientist to develop advanced analytics and ML solutions for user terminals in our LEO constellation. You will build predictive models to identify potential terminal failures in the field and collaborate with RF, antenna, silicon, algorithm, and software teams.

You will work in a cross-disciplinary environment, applying data-driven insights to inform design decisions and improve terminal performance across outdoor and indoor devices.

Qualifications

• Master's degree in STEM or equivalent experience.
• 3+ years SQL/Python or R/SAS/Matlab experience.
• 3+ years ML/statistical modeling and data analysis experience.
• Experience applying theoretical models in an applied environment.
• Experience conducting and documenting trade studies and design trades.
• Strong analytical, problem-solving, and communication skills.
• Ability to work in a small team and deliver results.

Responsibilities

• Develop statistical and analytical tools to enable regression decisions from on-orbit and lab measurements of user terminals.
• Publish documents and create visualizations to communicate insights to stakeholders.
• Create and manage datasets for pre-training and supervised fine-tuning of LLMs.
• Develop scalable visualizations for analysis of user terminal performance.
• Collaborate with engineers to root-cause failures using data as the primary tool.
• Drive consensus on metrics and analysis approaches to support product development strategy.

Skills

SQL

Python

R/SAS/Matlab

Education

Master's degree in STEM

PhD in STEM

Tools

R

SAS

MATLAB

Job description

Amazon Kuiper Manufacturing Enterprises LLC is seeking a Data Scientist to develop advanced analytics and ML solutions for user terminals in our LEO constellation. You will build predictive models to identify potential terminal failures in the field and collaborate with RF, antenna, silicon, algorithm, and software teams.

You will work in a cross-disciplinary environment, applying data-driven insights to inform design decisions and improve terminal performance across outdoor and indoor devices.