Trueanomalyinc Entry-level Flight Software Engineer role focused on data analysis and predictive modeling for spacecraft manufacturing and operations. Responsibilities include building dashboards, writing SQL queries, and training predictive models.
Responsibilities
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
Qualification
Proficiency in Python for data analysisCoursework in statisticsDebugging mindsetEagerness to learn manufacturingFamiliarity with time-series analysisExposure to data visualization tools
Required
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)
Preferred
Experience with machine learning in Python: scikit-learn for classification/regression, model validation, train/test splits, cross-validation