Job description

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 Experience building end-to-end data pipelines: data cleaning, feature engineering, model training, validation, and deployment Ability to communicate technical findings to non-technical stakeholders through clear visualizations and actionable recommendations Eagerness to learn manufacturing, operations, and reliability engineering domains where data science drives real operational improvements Passion for spaceflight and building reliable systems that perform in high-stakes environments PREFERRED SKILLS AND EXPERIENCE Experience with reliability engineering: survival analysis (Weibull, Cox models), reliability growth modeling, failure mode analysis Familiarity with manufacturing analytics: statistical process control (SPC), multivariate control charts, quality prediction from process data Exposure to anomaly detection techniques: Isolation Forest, LSTM autoencoders, change point detection, multivariate process monitoring Internship, research, or project experience in operations analytics, supply chain forecasting, or industrial IoT telemetry analysis Understanding of causal inference methods: directed acyclic graphs (DAGs), counterfactual reasoning, confounding variable analysis Experience with imbalanced classification: SMOTE, cost-sensitive learning, active learning for rare event prediction Familiarity with time-series forecasting: ARIMA, Prophet, exponential smoothing, handling regime changes and structural breaks Coursework or project work in operations research, queuing theory, optimization, or discrete event simulation Experience with text mining and NLP for log analysis, failure report clustering, or automated fault diagnosis COMPENSATION Base Salary: $125,000 - 270,000 Equity + Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, location, and experience. ADDITIONAL REQUIREMENTS Work Location —this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily. Work environment —the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job. Physical demands —the physical demands of the job, including bending, sitting, lifting and driving. This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. To protect candidates and our company from application fraud and impersonation, we use third-party tools to verify applicant identity and detect fraudulent applications. Learn more in our Privacy Notice .

Requirements

Must-haves

Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
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
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
Experience building end-to-end data pipelines: data cleaning, feature engineering, model training, validation, and deployment
Ability to communicate technical findings to non-technical stakeholders through clear visualizations and actionable recommendations
Eagerness to learn manufacturing, operations, and reliability engineering domains where data science drives real operational improvements
Passion for spaceflight and building reliable systems that perform in high-stakes environments
Experience with reliability engineering: survival analysis (Weibull, Cox models), reliability growth modeling, failure mode analysis
Familiarity with manufacturing analytics: statistical process control (SPC), multivariate control charts, quality prediction from process data
Exposure to anomaly detection techniques: Isolation Forest, LSTM autoencoders, change point detection, multivariate process monitoring
Internship, research, or project experience in operations analytics, supply chain forecasting, or industrial IoT telemetry analysis
Understanding of causal inference methods: directed acyclic graphs (DAGs), counterfactual reasoning, confounding variable analysis
Experience with imbalanced classification: SMOTE, cost-sensitive learning, active learning for rare event prediction
Familiarity with time-series forecasting: ARIMA, Prophet, exponential smoothing, handling regime changes and structural breaks
Coursework or project work in operations research, queuing theory, optimization, or discrete event simulation

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

pythonsql

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

401(K)

Pto

Parental Leave