Space Manufacturing Data Scientist: Predictive Analytics

Socket.dev; Denver (CO); On-site; USD 125,000 - 270,000; Full time

Benefits offered by this job

Health, dental, vision

Equity + Benefits

PTO and paid holidays

401K

Parental Leave

Job summary

True Anomaly in Denver is seeking a data scientist to build predictive models for spacecraft components, deploy real-time anomaly detection, and conduct data-driven root-cause analysis to keep missions on track.

The role emphasizes Python/SQL, end-to-end data pipelines, and collaboration with operations and reliability engineering teams in a spaceflight context. Onsite work in Denver with a strong focus on production diagnostics.

Qualifications

• 2–4 years of experience in a data science role, or a Master's with no experience.
• 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.

Responsibilities

• Build predictive models for component failure prediction using manufacturing telemetry, test data, and historical reliability records.
• Design and deploy anomaly detection systems for launch operations, environmental testing, and spacecraft integration.
• Perform root cause analysis on schedule delays, test failures, and quality escapes using causal inference, data mining, and statistical modeling.
• Develop data-driven diagnostic systems that fuse manufacturing history, supplier data, test logs, and failure reports.
• 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.

Skills

Python

SQL

Pandas

scikit-learn

Matplotlib

Time-series analysis

Data visualization

Statistical modeling

Communication

Education

Bachelor's degree in data science, statistics, industrial engineering, applied mathematics, operations research

Master's degree in one of these fields (optional with no experience)

Tools

Jupyter notebooks

Job description

True Anomaly in Denver is seeking a data scientist to build predictive models for spacecraft components, deploy real-time anomaly detection, and conduct data-driven root-cause analysis to keep missions on track.

The role emphasizes Python/SQL, end-to-end data pipelines, and collaboration with operations and reliability engineering teams in a spaceflight context. Onsite work in Denver with a strong focus on production diagnostics.