Software Engineer, True Anomaly

• Bachelor's or Master's degree in data science, statistics, industrial engineering, applied mathematics, operations research, or related quantitative field
• Proficiency in Python for data analysis: pandas, numpy, matplotlib, seaborn
• Working knowledge of SQL for querying relational databases: SELECT, JOIN, GROUP BY, aggregation functions
• Coursework in statistics: hypothesis testing, regression, probability distributions, experimental design
• Ability to create clear visualizations that communicate insights to technical and non-technical audiences
• Strong curiosity about how things fail and how data can predict failures before they happen
• Debugging mindset: when the model gives wrong answers or the query returns unexpected results, you dig in to find out why
• Eagerness to learn manufacturing, operations, and reliability engineering domains where data drives real decisions
• U.S. Citizen (required for facility access and government contracts)

Desirable

• This is a 3 month temporary employment engagement. There is potential to convert to regular employment based on performance and business need
• You'll turn spacecraft data into actionable insights across manufacturing and operations: building dashboards that surface production bottlenecks and on-orbit anomalies, analyzing test failures and mission telemetry to identify root causes, training predictive models that flag at-risk components before integration and detect spacecraft health degradation during missions, and mining telemetry to catch anomalies operators would miss
• Your work spans the full spacecraft lifecycle
• Pre-launch, you'll analyze manufacturing telemetry, test logs, failure reports, and supplier data to catch problems before integration
• Post-launch, you'll monitor on-orbit telemetry streams, detect anomalies in spacecraft health data, analyze mission performance, and flag degradation patterns that predict future failures
• This is entry-level data science work supporting hardware production and spacecraft operations
• You'll write SQL queries, build predictive models in Python, create operational dashboards, and see your analysis drive decisions on the manufacturing floor and in mission control
• Perform exploratory data analysis on manufacturing telemetry, test logs, mission data, and on-orbit spacecraft health telemetry to identify patterns and surface anomalies
• Build operational dashboards in Grafana or Plotly Dash showing real-time production status, spacecraft health metrics, mission performance, and anomaly alerts
• Train basic predictive models (logistic regression, random forests) to flag at-risk components during manufacturing and predict spacecraft health degradation during missions
• Write SQL queries to extract, join, and aggregate data from manufacturing databases, test systems, mission telemetry streams, and spacecraft health archives
• Analyze test failures and on-orbit anomalies to identify common failure modes, cluster similar issues, and quantify impact on schedule and mission success
• Create data visualizations (matplotlib, seaborn, Plotly) that communicate findings to engineers, manufacturing leads, mission operators, and program managers
• Implement statistical process control charts to detect out-of-spec conditions in manufacturing processes and spacecraft telemetry before they cascade
• Monitor on-orbit telemetry streams for anomalies: battery voltage trends, thermal behavior, attitude control health, communications link quality
• Document analysis methodology in Jupyter notebooks enabling reproducibility and knowledge transfer across manufacturing and operations teams
• Learn reliability engineering and mission operations concepts: failure modes, burn-in testing, on-orbit commissioning, spacecraft health monitoring, and anomaly response procedures