Space Data Scientist I: Predictive Analytics & Dashboards

Socket.dev; Denver (CO); Hybrid; USD 75,000 - 85,000; Full time

Job summary

True Anomaly in Denver is seeking an entry-level data scientist to transform spacecraft data into actionable insights across manufacturing and operations. You will write SQL queries, build predictive models in Python, and create dashboards to surface production bottlenecks and on-orbit anomalies.

You’ll monitor telemetry, analyze test failures, and train models to flag at-risk components before integration and during missions, supporting hardware production and spacecraft operations.

Qualifications

• Bachelor's or Master's degree in data science, statistics, industrial engineering, applied mathematics, or related quantitative field.
• Proficiency in Python for data analysis: pandas, numpy, matplotlib, seaborn.
• Working knowledge of SQL for querying relational databases: SELECT, JOIN, GROUP BY, aggregation functions.
• Coursework in statistics: hypothesis testing, regression, probability distributions, experimental design.
• Ability to create clear visualizations that communicate insights to technical and non-technical audiences.
• Strong curiosity about how things fail and how data can predict failures before they happen.
• Debugging mindset: when the model gives wrong answers or the query returns unexpected results, you dig in to find out why.
• Eagerness to learn manufacturing, operations, and reliability engineering domains where data drives real decisions.
• U.S. Citizen (required for facility access and government contracts).

Responsibilities

• Perform exploratory data analysis on manufacturing telemetry, test logs, mission data, and on-orbit spacecraft health telemetry to identify patterns and surface anomalies.
• Build operational dashboards in Grafana or Plotly Dash showing real-time production status, spacecraft health metrics, mission performance, and anomaly alerts.
• Train basic predictive models (logistic regression, random forests) to flag at-risk components during manufacturing and predict spacecraft health degradation during missions.
• Write SQL queries to extract, join, and aggregate data from manufacturing databases, test systems, mission telemetry streams, and spacecraft health archives.
• Analyze test failures and on-orbit anomalies to identify common failure modes, cluster similar issues, and quantify impact on schedule and mission success.
• Create data visualizations (matplotlib, seaborn, Plotly) that communicate findings to engineers, manufacturing leads, mission operators, and program managers.
• Implement statistical process control charts to detect out-of-spec conditions in manufacturing processes and spacecraft telemetry before they cascade.
• Monitor on-orbit telemetry streams for anomalies: battery voltage trends, thermal behavior, attitude control health, communications link quality.
• Document analysis methodology in Jupyter notebooks enabling reproducibility and knowledge transfer across manufacturing and operations teams.
• Learn reliability engineering and mission operations concepts: failure modes, burn-in testing, on-orbit commissioning, spacecraft health monitoring, and anomaly response procedures.

Skills

Python

SQL

Pandas

Matplotlib

Seaborn

Grafana

Plotly Dash

Education

Bachelor's or Master's in data science or related

Tools

Grafana

Tableau

Plotly Dash

Job description

True Anomaly in Denver is seeking an entry-level data scientist to transform spacecraft data into actionable insights across manufacturing and operations. You will write SQL queries, build predictive models in Python, and create dashboards to surface production bottlenecks and on-orbit anomalies.

You’ll monitor telemetry, analyze test failures, and train models to flag at-risk components before integration and during missions, supporting hardware production and spacecraft operations.