Data Scientist (II-Senior), Manufacturing Analytics

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. BENEFITS & PERKS * Best-in-class healthcare: 100% company-paid medical, dental, and vision coverage for you and your dependents, with HSA/HRA options Generous time off: Flexible Time Off (FTO) for exempt employees, accrued PTO of up to 25 days for non-exempt employees, plus 12 paid holidays; Parental Leave; Sick Leave Fuel for your day: Daily catered lunch and unlimited snacks for in-office employees And more: 401(k) retirement plan, fitness reimbursement, and Dog Fridays Equity participation: Equity is included as part of our total compensation package, giving you an opportunity to share in the company’s success. *Do not apply to fixed term and temporary roles YOUR MISSION You'll build predictive models that catch component failures before they impact missions by analyzing manufacturing telemetry, deploy real-time anomaly detection systems for testing and operations that flag deviations operators would miss, and investigate schedule slips and quality issues using data-driven root cause analysis to distinguish signal from noise. You'll mine historical production data to identify bottlenecks, optimize test durations, and reduce rework, while scoring supplier reliability and predicting delivery delays to flag at-risk components. Throughout, you'll develop integrated diagnostic tools that fuse logs, telemetry, and historical data to narrow failure root causes and accelerate engineering investigations that keep spacecraft on schedule and missions on track. 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   QUALIFICATIONS 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   PREFERRED SKILLS AND EXPERIENCE 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   COMPENSATION