Senior Data Scientist (Manufacturing Analytics)

Join True Anomaly, a pioneering space security company, as a Senior Data Scientist specializing in manufacturing analytics. In this role, you will build predictive models to catch component failures before they impact missions, deploy real-time anomaly detection systems, and investigate schedule slips and quality issues using data-driven root cause analysis. You will also mine historical production data to identify bottlenecks, optimize test durations, and reduce rework. This is a fully onsite position based in Denver or Long Beach.

Work Location—this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily

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

Responsibilities

• Construire des modèles prédictifs pour la prédiction des pannes des composants en utilisant la télémétrie de fabrication, les données d'essai et les dossiers de fiabilité historiques.
• Concevoir et déployer des systèmes de détection d'anomalies pour les opérations de lancement, les tests environnementaux et l'intégration des engins spatiaux qui signalent les écarts en temps réel.
• Effectuer une analyse des causes profondes des retards de calendrier, des échecs de test et des échappements de qualité en utilisant l'inférence causale, l'exploration de données et la modélisation statistique.
• Ability to communicate technical findings to non-technical stakeholders through clear visualizations and actionable recommendations
• 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
• Eagerness to learn manufacturing, operations, and reliability engineering domains where data science drives real operational improvements
• Experience building end-to-end data pipelines: data cleaning, feature engineering, model training, validation, and deployment
• Understanding of causal inference methods: directed acyclic graphs (DAGs), counterfactual reasoning, confounding variable analysis

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
• Passion for spaceflight and building reliable systems that perform in high-stakes environments
• Exposure to anomaly detection techniques: Isolation Forest, LSTM autoencoders, change point detection, multivariate process monitoring
• Coursework or project work in operations research, queuing theory, optimization, or discrete event simulation
• Internship, research, or project experience in operations analytics, supply chain forecasting, or industrial IoT telemetry analysis
• Experience with reliability engineering: survival analysis (Weibull, Cox models), reliability growth modeling, failure mode analysis
• Familiarity with time-series forecasting: ARIMA, Prophet, exponential smoothing, handling regime changes and structural breaks

Preferred

• Familiarity with manufacturing analytics: statistical process control (SPC), multivariate control charts, quality prediction from process data
• Experience with imbalanced classification: SMOTE, cost-sensitive learning, active learning for rare event prediction
• Experience with text mining and NLP for log analysis, failure report clustering, or automated fault diagnosis

Pay

USD 125,000 - 270,000