Senior Data Scientist, True Anomaly

• 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
• 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

Desirable

• 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
• 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

• This position will be open until it is successfully filled. To submit your application, please follow the directions below