Space Data Analytics Engineer I

Trueanomalyinc; Los Angeles (CA); Hybrid; USD 75,000 - 80,000; Full time

Job summary

True Anomaly seeks data scientists to 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.

Qualifications

• 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 (requiredfor facility access and government contracts)

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 PlotlyDash 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

Skills

Python for data analysis

SQL

Data visualization

Jupyter

Statistics

scikit-learn

Time-series

Grafana

Plotly Dash

MTBF

Data cleaning

A/B testing

Anomaly detection

Manufacturing data

Git

Industrial engineering

Operations research

Tableau

Education

BS/MS in data science

Tools

Grafana

Tableau

Plotly Dash

Git

Job description

True Anomaly seeks data scientists to 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.