SpaceX Full-Stack Data Scientist at SpaceX Starlink building data pipelines, models, and production scoring systems to improve hardware reliability. Works with telemetry, manufacturing, and unstructured support data.
Responsibilities
Build and maintain data pipelines, infrastructure, and software to objectively increase the reliability of Starlink customer hardware
Design and operate the feature, label, and scoring data that reliability models train and serve on, including backfills, late-arriving data, and data quality
Train, validate, and iterate models for hardware failure prediction both in production line and field
Deploy and operate models in production, including scheduled scoring, versioning, monitoring, and iteration based on live results
Contribute to critical Starlink software repositories, working in core internal and customer-facing codebases
Build internal analytics tools, APIs, and applications used by hardware, production, quality, supply chain, and customer experience teams
Collaborate across engineering, production, test, inventory, quality, supply chain, and customer experience to turn analyses and predictions into design, process, and support changes
Identify customer needs, gather requirements, and execute independently in a dynamic environment with changing constraints and tight deadlines
Qualification
Bachelor’s degree in computer scienceStrong proficiency in Python and SQLMaster’s degree in computer scienceExperience with relational databasesExperience deployingStrong analytical skills
Required
Bachelor’s degree in computer science, data science, mathematics, electrical engineering, or other STEM discipline
4+ years of professional experience in data science, machine learning, or software engineering
Strong proficiency in Python and SQL
Experience building production data pipelines
Experience deploying machine learning models in production
Preferred
Master’s degree in computer science, data science, mathematics, or other STEM discipline (or PhD with 3+ years of relevant experience)
Strong understanding of software engineering fundamentals including data structures, algorithms, system design, concurrency, and writing high-performance, maintainable code
Experience with relational databases, data pipeline orchestration, and large-scale data processing
Experience deploying, versioning, and monitoring machine learning models in production, including scheduled scoring jobs whose outputs are consumed by other systems
Experience building APIs or internal tools that deliver model outputs to engineering users
Experience shipping large language models in production, including retrieval-augmented generation (RAG), tool calling, and agentic workflows
Experience extracting signal from unstructured data (support tickets, chat, call transcripts, images) alongside structured telemetry and field-return data
Strong analytical skills: statistical analysis, data visualization, quantitative reasoning, and reliability methods (survival analysis, calibration, experimental design)
