Senior Data Scientist (Manufacturing Analytics)

True Anomaly; Long Beach (CA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Healthcare (Employer-Sponsored)

Time off

Equity

401k and Roth 401k

Learning & Development Opportunities

Job summary

True Anomaly seeks a data scientist to build predictive models from manufacturing telemetry, test data, and reliability records. You will develop anomaly detection systems for launch operations and integrate diagnostic tools across production and testing to reduce failures and schedule slips.

The role requires strong Python/SQL skills, experience with time-series and survival analysis, and the ability to translate complex analyses into actionable recommendations for cross-functional teams at our

Qualifications

• Bachelor’s degree in data science, statistics, industrial engineering, applied mathematics, operations research, or similar quantitative discipline; 2–4 years of experience.
• Strong Python, SQL, and data visualization skills.
• Experience building end-to-end data pipelines: cleaning, feature engineering, model training, validation, deployment.

Responsibilities

• Build predictive models to catch component failures using manufacturing telemetry and test data.
• Design and deploy real-time anomaly detection for testing and operations.
• Perform data-driven root cause analysis to distinguish signal from noise.
• Develop integrated diagnostic tools fusing logs, telemetry, and historical data.
• Create dashboards for real-time situational awareness across production, test, and integration workflows.
• Communicate findings to non-technical stakeholders with clear visuals.

Skills

Python

SQL

Pandas

Scikit-learn

Matplotlib

Time-series analysis

Statistical modeling

Data visualization

Communication

Education

Bachelor's degree in data science, statistics, industrial engineering, or related quantitative field

Master’s degree in a similar field (optional with 0+ experience)

Tools

Python

Scikit-learn

Matplotlib

SQL

Jupyter

Job description

• 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
• 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

Benefits

• 100% Employer-Sponsored Healthcare: We’ve got you covered! Our comprehensive healthcare package is fully sponsored, ensuring you and your loved ones stay healthy and worry-free, so you can focus on shaping the future of space security.
• Generous Time Off: We believe in working hard and recharging fully. Our generous vacation policy gives you the flexibility to take the time you need to relax, explore, and come back energized to take on new challenges.
• Equity: Own an equity stake in True Anomaly as you build something transformative. With equity options, you’re not just working for True Anomaly; you’re an owner. As an essential part of our growth and future success, we want you to share in the upside potential!
• 401k and Roth 401k options: Plan for tomorrow with our 401K and Roth 401K options that put you in control of your financial future.
• Learning & Development Opportunities: Your growth fuels our innovation. We’re committed to providing you with the resources and training to expand your skills, spark new ideas, and advance your career with us.

Ability to communicate technical findings to non-technical stakeholders through clear visualizations and actionable recommendationsProficient in Python (pandas, scikit-learn, matplotlib) and SQL for data manipulation, analysis, and visualizationStrong statistical fundamentals: hypothesis testing, regression, time-series analysis, survival analysis, and experimental designEagerness to learn manufacturing, operations, and reliability engineering domains where data science drives real operational improvementsBachelor’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 requiredPassion for spaceflight and building reliable systems that perform in high-stakes environmentsExperience building end-to-end data pipelines: data cleaning, feature engineering, model training, validation, and deploymentWork Location-this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office dailyWork environment-the work environment; temperature, noise level, inside or outside, or other factors that will affect the person’s working conditions while performing the jobPhysical demands-the physical demands of the job, including bending, sitting, lifting and drivingExposure to anomaly detection techniques: Isolation Forest, LSTM autoencoders, change point detection, multivariate process monitoringUnderstanding of causal inference methods: directed acyclic graphs (DAGs), counterfactual reasoning, confounding variable analysisCoursework or project work in operations research, queuing theory, optimization, or discrete event simulationInternship, research, or project experience in operations analytics, supply chain forecasting, or industrial IoT telemetry analysisExperience with reliability engineering: survival analysis (Weibull, Cox models), reliability growth modeling, failure mode analysisFamiliarity with manufacturing analytics: statistical process control (SPC), multivariate control charts, quality prediction from process dataExperience with imbalanced classification: SMOTE, cost-sensitive learning, active learning for rare event predictionFamiliarity with time-series forecasting: ARIMA, Prophet, exponential smoothing, handling regime changes and structural breaksExperience with text mining and NLP for log analysis, failure report clustering, or automated fault diagnosis