Trainee Full Stack Data Scientist Jobs in Texas (NOW HIRING)

Full-Stack Data Scientist, Hardware Reliability (Starlink)

Bastrop, TX

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal ofenabling human life on Mars.

SpaceX is leveraging our experience building rockets and spacecraft to deploy Starlink, the world’s largest satellite constellation and most advanced broadband internet system. We provide reliable and fast internet to millions of users worldwide, including populations with little or no connectivity, rural communities, aircraft, watercraft, and places where existing services are unreliable, too expensive, or disconnected by natural disasters. We design, build, test, and operate all parts of the system, thousands of satellites and millions of consumer antennae that allow users to connect within minutes of unboxing. The Starlink team is seeking out the best-in-class professionals to maximize Starlink’s potential for communities and businesses around the globe.

As a Full-stack Data Scientist on the Starlink Reliability team, you will build the data pipelines, models, and production scoring systems that improve the reliability of Starlink customer hardware (dishes, routers, power supplies, cables). You will work from telemetry, manufacturing, inventory, field-return, and unstructured support data (tickets, chats, images) through production pipelines and deployed models, put scores into the workflows that drive replacements and support decisions, and build LLM-backed tools — retrieval, tool calling, and agentic workflows — that engineers and operators run. You will measure whether those systems reduce failures as we launch new products and scale the network. The team is based in Bastrop, on site with the factory that builds this hardware.

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

BASIC QUALIFICATIONS:

• 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 deploying machine learning models in production

PREFERRED SKILLS AND EXPERIENCE:

• 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)
• Familiar with at least one additional programming