Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors — enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES
- Be the offset. We create asymmetric advantages with creativity and ingenuity.
- What would it take? We challenge assumptions to deliver ambitious results.
- It’s the people. Our team is our competitive advantage and we are better together.
YOUR MISSION
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.
This is a 3 month temporary employment engagement. There is potential to convert to regular employment based on performance and business need.
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
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 (required for facility access and government contracts)
PREFERRED SKILLS AND EXPERIENCE
- Experience with machine learning in Python: scikit-learn for classification/regression, model validation, train/test splits, cross-validation
- Familiarity with time-series analysis: plotting sensor trends, detecting change points, smoothing noisy signals
- Exposure to data visualization tools: Grafana, Tableau, Plotly Dash, or similar dashboard frameworks
- Understanding of basic reliability concepts: failure rates, survival curves, mean time between failures (MTBF)
- Prior internship or project analyzing real-world operational data: manufacturing, logistics, quality control, IoT sensor data
- Experience with version control (git) and collaborative data analysis workflows
- Coursework or projects in industrial engineering, operations research, or quality management
- Familiarity with data cleaning and wrangling: handling missing values, outlier detection, data quality assessment
- Understanding of experimental design: A/B testing, randomized controlled trials, confounding variables
- Exposure to anomaly detection techniques: z-scores, control charts, boxplot analysis
- Prior work with manufacturing or hardware production data (even from coursework or academic projects)
- Familiarity with Jupyter notebooks, literate programming, and reproducible analysis practices
COMPENSATION
- Base Salary: Denver: $75,000; Long Beach: $80,000
ADDITIONAL REQUIREMENTS
- Work Location—Successful candidates will be located near Denver or Colorado Springs. While we observe a hybrid work environment, some work must be done on site.
- 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.
This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State.
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.
Skills Required
- Bachelor's or Master's degree in data science, statistics, industrial engineering, applied mathematics, operations research, or a related quantitative field
- Proficiency in Python for data analysis, including pandas, NumPy, Matplotlib, and Seaborn
- Working knowledge of SQL, including SELECT, JOIN, GROUP BY, and aggregation functions
- Coursework in statistics, including hypothesis testing, regression, probability distributions, and experimental design
- Ability to create clear visualizations for technical and non-technical audiences
- Strong curiosity about failure analysis and predictive data insights
- Debugging mindset and ability to investigate incorrect model results or unexpected query results
- Eagerness to learn manufacturing, operations, and reliability engineering domains
- U.S. citizenship required for facility access and government contracts
- Experience with machine learning in Python, including scikit-learn, classification or regression, model validation, train/test splits, and cross-validation
- Familiarity with time-series analysis, sensor trends, change-point detection, and smoothing noisy signals
- Exposure to Grafana, Tableau, Plotly Dash, or similar dashboard frameworks
- Understanding of reliability concepts including failure rates, survival curves, and mean time between failures
- Prior internship or project analyzing real-world operational data
- Experience with Git and collaborative data analysis workflows
- Coursework or projects in industrial engineering, operations research, or quality management
- Familiarity with data cleaning, missing values, outlier detection, and data quality assessment
- Understanding of experimental design, A/B testing, randomized controlled trials, and confounding variables
- Exposure to anomaly detection techniques such as z-scores, control charts, and boxplot analysis
- Prior work with manufacturing or hardware production data
- Familiarity with Jupyter notebooks, literate programming, and reproducible analysis practices
What We Do
Space was once the quietest place in the universe. Now, it's crowded, contested, and confrontational. We are True Anomaly: the only defense company focused exclusively on space defense. Founded in 2022 by ex-U.S. Space Force members, True Anomaly designs and builds advanced systems for space superiority: agile and powerful spacecraft platforms, mission software engineered for unmatched command and control, and payloads tailored for precision sensing and effects. True Anomaly is headquartered in Centennial, CO, with regional offices in Colorado Springs, CO, Long Beach, CA, and Washington, D.C. We are hiring and seeking exceptional talent to join True Anomaly, from any technical industry or background, to bring unique talents, perspective, and solutions. If you embrace complexity, lead instead of follow, showcase integrity over ego, take ownership for outcomes, and measure success by impact, we want to hear from you.
Why Work With Us
True Anomaly exists to enable a secure, stable, and sustainable space environment for the US, its allies, and partners. We design and build spacecraft and software solutions for space superiority. If you are ready to contribute to the future of space defense, we’d love to hear from you.
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True Anomaly Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
We are a hybrid office culture. Our employees work from offices in Denver, Colorado Springs, Los Angeles, and Washington D.C. We recognize the importance of in-office collaboration, but also the value of a flexible work and location requirements
