Software Engineering / Technology

Data ScientistSpace is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors - enabling the U.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. 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.Perform exploratory data analysis on manufacturing telemetry, test logs, mission data, and on-orbit spacecraft health telemetry to identify patterns and surface anomaliesBuild operational dashboards in Grafana or Plotly Dash showing real-time production status, spacecraft health metrics, mission performance, and anomaly alertsTrain basic predictive models (logistic regression, random forests) to flag at-risk components during manufacturing and predict spacecraft health degradation during missionsWrite SQL queries to extract, join, and aggregate data from manufacturing databases, test systems, mission telemetry streams, and spacecraft health archivesAnalyze test failures and on-orbit anomalies to identify common failure modes, cluster similar issues, and quantify impact on schedule and mission successCreate data visualizations (matplotlib, seaborn, Plotly) that communicate findings to engineers, manufacturing leads, mission operators, and program managersImplement statistical process control charts to detect out-of-spec conditions in manufacturing processes and spacecraft telemetry before they cascadeMonitor on-orbit telemetry streams for anomalies: battery voltage trends, thermal behavior, attitude control health, communications link qualityDocument analysis methodology in Jupyter notebooks enabling reproducibility and knowledge transfer across manufacturing and operations teamsLearn reliability engineering and mission operations concepts: failure modes, burn-in testing, on-orbit commissioning, spacecraft health monitoring, and anomaly response proceduresQUALIFICATIONSBachelor's or Master's degree in data science, statistics, industrial engineering, applied mathematics, operations research, or related quantitative fieldProficiency in Python for data analysis: pandas, numpy, matplotlib, seabornWorking knowledge of SQL for querying relational databases: SELECT, JOIN, GROUP BY, aggregation functionsCoursework in statistics: hypothesis testing, regression, probability distributions, experimental designAbility to create clear visualizations that communicate insights to technical and non-technical audiencesStrong curiosity about how things fail and how data can predict failures before they happenDebugging mindset: when the model gives wrong answers or the query returns unexpected results, you dig in to find out whyEagerness to learn manufacturing, operations, and reliability engineering domains where data drives real decisionsU.Citizen (required for facility access and government contracts)PREFERRED SKILLS AND EXPERIENCEExperience with machine learning in Python: scikit-learn for classification/regression, model validation, train/test splits, cross-validationFamiliarity with time-series analysis: plotting sensor trends, detecting change points, smoothing noisy signalsExposure to data visualization tools: Grafana, Tableau, Plotly Dash, or similar dashboard frameworksUnderstanding 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 dataExperience with version control (git) and collaborative data analysis workflowsCoursework or projects in industrial engineering, operations research, or quality managementFamiliarity with data cleaning and wrangling: handling missing values, outlier detection, data quality assessmentUnderstanding of experimental design: A/B testing, randomized controlled trials, confounding variablesExposure to anomaly detection techniques: z-scores, control charts, boxplot analysisPrior work with manufacturing or hardware production data (even from coursework or academic projects)Familiarity with Jupyter notebooks, literate programming, and reproducible analysis practicesCOMPENSATIONBase Salary: Denver: $75,000; While we observe a hybrid work environment, some work must be done on site.To submit your application, please follow the directions below.