Software Engineer I, Data Science (New Grad)

True Anomaly•Los Angeles, CA

4h•Hybrid

About The Position

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.

Requirements

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)

Nice To Haves

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

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