Staff Engineer, Data Analysis (R5290)

Shield AI•San Diego, CA

8d•$150,000 - $220,000•Onsite

About The Position

Shield AI is seeking a Staff Engineer to lead the data analysis efforts for its V-BAT fleet. The V-BAT system generates extensive data from various sources including real-world flight tests, fielded aircraft, production activities, and simulations. The successful candidate will be responsible for the analysis pipelines, tooling, standards, and metrics used to understand fleet-wide system performance derived from software and hardware systems. This role involves conducting in-depth studies on critical aspects like sensor performance, navigation, communications, and other key aircraft behaviors. The Staff Engineer will collaborate closely with DevOps, Flight Software, Flight Test, Production, Quality, and Operations teams to transform complex aircraft data into actionable engineering insights.

Requirements

5+ years of relevant experience with a Bachelor’s degree in Computer Science, Data Science, or a related technical field
Strong Python skills and experience building data analysis tools, automated analysis workflows, or data pipelines
Experience analyzing data from fielded physical products such as aerospace systems, automotive systems, robotics platforms, commercial electronics, IoT devices, industrial equipment, or similarly complex real-world systems
Experience working with large, messy datasets from deployed products, test events, simulations, production systems, or operational environments
Ability to translate ambiguous engineering questions into structured analysis, sound conclusions, and clear recommendations
Strong communication skills and the ability to influence cross-functional engineering, production, quality, and operations teams

Nice To Haves

Experience with flight test data, aircraft telemetry, UAVs, robotics, autonomy, embedded systems, GNC, navigation systems, communications systems, or aerospace sensor suites
Experience with Matlab, Jupyter, or similar analysis and visualization tools
Experience building production-quality dashboards, automated reports, data products, or fleet-health monitoring systems
Experience with databases, cloud storage, data lake architectures, time-series databases, telemetry systems, or data cataloging
Experience with anomaly detection, trend analysis, statistical process control, reliability analysis, regression detection, or automated validation of complex systems

Responsibilities

Lead the technical strategy for V-BAT fleet data analysis across fielded aircraft, flight test, simulation, production, and quality workflows
Own and improve the pipelines that transform raw flight, simulation, and fleet data into reliable engineering metrics, reports, and analysis products
Conduct fleet-wide studies to identify trends in hardware quality, system performance, reliability, and operational behavior
Analyze flight-critical sensor performance, GNSS-denied navigation performance, communications behavior, and other aircraft subsystems that are critical to mission success
Standardize the team’s analysis methods across online tools, Jupyter notebooks, automated Python scripts, and legacy Matlab workflows
Define best practices for analysis methods, data review, documentation, validation, reproducibility, and contribution workflows
Build automated Python workflows that make high-value metrics easily accessible to engineering, production, quality, and leadership teams
Partner with DevOps to build, deploy, maintain, and scale the infrastructure required for automated analysis pipelines and dashboards
Work with Software, GNC, Embedded, Flight Test, Systems, Production, Quality, and Operations teams to define metrics that reflect aircraft performance and product health
Support anomaly investigations, root-cause analysis, release readiness, production quality improvements, and customer-impacting fleet investigations with rigorous data analysis
Mentor engineers on effective data analysis practices and raise the quality bar for data-driven engineering decisions across the V-BAT team