Analyze fleet, vehicle, and operational data to support reliability, safety validation, and service readiness initiatives
Partner with Reliability Engineering and Mission Assurance to help define and track key performance metrics
Build and scale end-to-end predictive frameworks to monitor component/subsystem health, degradation trends, and real-world usage patterns.
Build and maintain data pipelines, queries, and dashboards to enable visibility into system health and performance
Perform statistical analyses to identify trends, anomalies, and improvement opportunities across hardware and software systems
Support investigations into vehicle issues by cleaning, aggregating, and visualizing large datasets
Contribute to best practices around data quality, reproducibility, and analytical rigor
Communicate findings clearly to technical stakeholders through written summaries, visualizations, and presentations

Requirements

1–3 years of professional experience (or equivalent academic/project experience) in data science, analytics, reliability engineering, or a related field
Working proficiency in SQL for querying relational databases
Working proficiency in Python and common data analysis libraries (Pandas, NumPy, SciPy)
Foundational understanding of statistics and the ability to communicate uncertainty and assumptions
Experience building analyses, metrics, or dashboards to support engineering or business decisions
Familiarity with version control systems (Git).
General knowledge of physics and engineering principles.
Strong communication skills and ability to collaborate across engineering teams

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

data analysisstatistical analysisSQLPythonPandasNumPySciPydata visualizationdata pipeline developmentpredictive modeling

Soft Skills

communication skillscollaborationanalytical rigorproblem-solvingattention to detailpresentation skillsstakeholder engagementteamworkadaptabilitycritical thinking