Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.

OUR MISSION

True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors — enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.

OUR VALUES

Be the offset. We create asymmetric advantages with creativity and ingenuity.
What would it take? We challenge assumptions to deliver ambitious results.
It’s the people. Our team is our competitive advantage and we are better together.

BENEFITS & PERKS*

Best-in-class healthcare: 100% company-paid medical, dental, and vision coverage for you and your dependents, with HSA/HRA options
Generous time off: Flexible Time Off (FTO) for exempt employees, accrued PTO of up to 25 days for non-exempt employees, plus 12 paid holidays; Parental Leave; Sick Leave
Fuel for your day: Daily catered lunch and unlimited snacks for in-office employees
And more: 401(k) retirement plan, fitness reimbursement, and Dog Fridays
Equity participation: Equity is included as part of our total compensation package, giving you an opportunity to share in the company’s success.

*Do not apply to fixed term and temporary roles

YOUR MISSION

You'll build predictive models that catch component failures before they impact missions by analyzing manufacturing telemetry, deploy real-time anomaly detection systems for testing and operations that flag deviations operators would miss, and investigate schedule slips and quality issues using data-driven root cause analysis to distinguish signal from noise. You'll mine historical production data to identify bottlenecks, optimize test durations, and reduce rework, while scoring supplier reliability and predicting delivery delays to flag at-risk components. Throughout, you'll develop integrated diagnostic tools that fuse logs, telemetry, and historical data to narrow failure root causes and accelerate engineering investigations that keep spacecraft on schedule and missions on track.

RESPONSIBILITIES

Build predictive models for component failure prediction using manufacturing telemetry, test data, and historical reliability records to catch issues before they impact missions
Design and deploy anomaly detection systems for launch operations, environmental testing, and spacecraft integration that flag deviations in real-time without overwhelming operators with false alarms
Perform root cause analysis on schedule delays, test failures, and quality escapes using causal inference, data mining, and statistical modeling to identify actionable improvement opportunities
Develop data-driven diagnostic systems that fuse manufacturing history, supplier data, test logs, and failure reports to narrow root causes and accelerate troubleshooting
Build and maintain operational dashboards providing real-time situational awareness across production, test, and integration workflows
Mine historical test and production data to identify patterns, cluster failure modes, prioritize process improvements, and quantify risk for upcoming builds
Implement statistical process control and quality monitoring systems that detect out-of-spec conditions before they propagate downstream
Write clear, maintainable Python/SQL code and Jupyter notebooks that document analysis methodology and enable reproducibility across the engineering team
Learn and grow alongside operations, manufacturing, and reliability engineers, translating business questions into data solutions that drive decisions

QUALIFICATIONS

Bachelor's degree in data science, statistics, industrial engineering, applied mathematics, operations research, or a similar quantitative discipline, plus 2-4 years of experience; or a Master's degree in one of these fields with no experience required.
Proficient in Python (pandas, scikit-learn, matplotlib) and SQL for data manipulation, analysis, and visualization
Strong statistical fundamentals: hypothesis testing, regression, time-series analysis, survival analysis, and experimental design