Data Scientist (II-Senior), Operations & Reliability

True Anomaly•Denver, CO

4d•Onsite

About The Position

True Anomaly seeks talented and ambitious individuals to build technology that secures space. We deliver decisive capabilities for space superiority, building autonomous spacecraft, advanced payloads, mission software, and space-based interceptors. This role focuses on building predictive models to anticipate component failures, deploying real-time anomaly detection systems, and conducting data-driven root cause analysis for operational and production issues. The goal is to optimize processes, improve reliability, and ensure spacecraft missions stay on schedule.

Requirements

Bachelor's degree in data science, statistics, industrial engineering, applied mathematics, operations research, or a similar quantitative discipline, plus 2-4 years of experience; or a Master's degree in one of these fields with no experience required.
Proficient in Python (pandas, scikit-learn, matplotlib) and SQL for data manipulation, analysis, and visualization.
Strong statistical fundamentals: hypothesis testing, regression, time-series analysis, survival analysis, and experimental design.
Experience building end-to-end data pipelines: data cleaning, feature engineering, model training, validation, and deployment.
Ability to communicate technical findings to non-technical stakeholders through clear visualizations and actionable recommendations.
Eagerness to learn manufacturing, operations, and reliability engineering domains where data science drives real operational improvements.
Passion for spaceflight and building reliable systems that perform in high-stakes environments.
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.

Nice To Haves

Experience with reliability engineering: survival analysis (Weibull, Cox models), reliability growth modeling, failure mode analysis.
Familiarity with manufacturing analytics: statistical process control (SPC), multivariate control charts, quality prediction from process data.
Exposure to anomaly detection techniques: Isolation Forest, LSTM autoencoders, change point detection, multivariate process monitoring.
Internship, research, or project experience in operations analytics, supply chain forecasting, or industrial IoT telemetry analysis.
Understanding of causal inference methods: directed acyclic graphs (DAGs), counterfactual reasoning, confounding variable analysis.
Experience with imbalanced classification: SMOTE, cost-sensitive learning, active learning for rare event prediction.
Familiarity with time-series forecasting: ARIMA, Prophet, exponential smoothing, handling regime changes and structural breaks.
Coursework or project work in operations research, queuing theory, optimization, or discrete event simulation.
Experience with text mining and NLP for log analysis, failure report clustering, or automated fault diagnosis.

Responsibilities

Build predictive models for component failure prediction using manufacturing telemetry, test data, and historical reliability records to catch issues before they impact missions.
Design and deploy anomaly detection systems for launch operations, environmental testing, and spacecraft integration that flag deviations in real-time without overwhelming operators with false alarms.
Perform root cause analysis on schedule delays, test failures, and quality escapes using causal inference, data mining, and statistical modeling to identify actionable improvement opportunities.
Develop data-driven diagnostic systems that fuse manufacturing history, supplier data, test logs, and failure reports to narrow root causes and accelerate troubleshooting.
Build and maintain operational dashboards providing real-time situational awareness across production, test, and integration workflows.
Mine historical test and production data to identify patterns, cluster failure modes, prioritize process improvements, and quantify risk for upcoming builds.
Implement statistical process control and quality monitoring systems that detect out-of-spec conditions before they propagate downstream.
Write clear, maintainable Python/SQL code and Jupyter notebooks that document analysis methodology and enable reproducibility across the engineering team.
Learn and grow alongside operations, manufacturing, and reliability engineers, translating business questions into data solutions that drive decisions.