Space Manufacturing Analytics Data Scientist

Trueanomalyinc; Denver, California (CO, MO); On-site; USD 125,000 - 270,000; Full time

Benefits offered by this job

Equity + Benefits including Health, D,

Job summary

True Anomaly in Denver is seeking a data-focused engineer to build predictive models for component failures using manufacturing telemetry and real-time anomaly detection for testing and operations.

You will perform data-driven root cause analysis, mine historical production data, and develop integrated diagnostic tools to quickly narrow failure root causes and accelerate engineering investigations, keeping spacecraft on track.

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 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

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
• 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

Skills

Python programming

SQL

Data visualization

Clear communication

Eagerness to learn manufacturing/ops

Spaceflight passion

Education

Bachelor's degree in data science or quantitative field

Master's degree with no experience required

Tools

Pandas

Scikit-learn

Matplotlib

Jupyter notebooks

Job description

True Anomaly in Denver is seeking a data-focused engineer to build predictive models for component failures using manufacturing telemetry and real-time anomaly detection for testing and operations.

You will perform data-driven root cause analysis, mine historical production data, and develop integrated diagnostic tools to quickly narrow failure root causes and accelerate engineering investigations, keeping spacecraft on track.