LEO Terminal Data Scientist: Predictive ML & Analytics

Amazon Science

Redmond (WA)

On-site

USD 136,000 - 184,000

Full time

14 days+
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Benefits offered by this job

Health insurance
RSUs
401(k) matching
Paid time off
Parental leave

Job summary

Amazon Science is seeking a Data Scientist to build advanced analytics and ML solutions for user terminals in the LEO program. You will predict failures, develop data pipelines, and collaborate with cross-disciplinary teams including constellation, RF, silicon, and software engineers.

The role emphasizes data-driven decision making, scalable visualizations, and producing insights for product development strategy in a fast-paced space environment.

Qualifications

  • Master's degree in STEM or equivalent experience in STEM fields.
  • 3+ years of experience with data querying languages (SQL) and scripting (Python) or statistical software (R/SAS/Matlab).
  • 3+ years of ML/statistical modeling experience and applied use of theoretical models.
  • Experience conducting trade studies and design trades; strong communication skills.

Responsibilities

  • Develop analytical tools for a team building current and future user terminals.
  • Publish documents and create visualizations to communicate insights to stakeholders.
  • Create and manage datasets for pre-training and fine-tuning of LLMs.
  • Develop scalable visualizations for analysis of terminal performance.
  • Collaborate with engineers to root-cause failures using data as the primary tool.

Skills

SQL
Python
R
SAS
Matlab
Perl

Education

Master's degree in STEM
PhD in STEM (preferred)

Tools

Git
TensorFlow

Job description

Amazon Science is seeking a Data Scientist to build advanced analytics and ML solutions for user terminals in the LEO program. You will predict failures, develop data pipelines, and collaborate with cross-disciplinary teams including constellation, RF, silicon, and software engineers.

The role emphasizes data-driven decision making, scalable visualizations, and producing insights for product development strategy in a fast-paced space environment.

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