About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Scientific & QA.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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About the Role & Mission

Confidential Client delivers capabilities for space systems and operations that support mission success across manufacturing, testing, and on-orbit operations. As a Software Engineer I (Data Science) — New Grad — you will turn spacecraft and manufacturing data into actionable insights: build dashboards that surface production bottlenecks and on-orbit anomalies, analyze test failures and mission telemetry to identify root causes, train predictive models to flag at-risk components, and help monitor spacecraft health during missions. This is an entry-level, hands-on data science role supporting hardware production and spacecraft operations.

This is a 3-month temporary contract position with potential to convert to regular employment based on performance and business need.

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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 (Grafana, Plotly Dash, or similar) 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 to predict spacecraft health degradation
• Write SQL 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 impacts 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
• Monitor on-orbit telemetry streams for anomalies (battery voltages, thermal behavior, attitude control health, communications link quality)
• Document analysis methodology in Jupyter notebooks for reproducibility and knowledge transfer
• Learn reliability engineering and mission operations concepts (failure modes, burn-in testing, on-orbit commissioning, anomaly response)

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Qualifications

• Bachelor’s or Master’s degree in data science, statistics, industrial engineering, applied mathematics, operations research, or a related quantitative field
• Proficiency in Python for data analysis: pandas, numpy, matplotlib, seaborn
• Working knowledge of SQL (SELECT, JOIN, GROUP BY, aggregation functions)
• Coursework or demonstrated understanding of statistics: hypothesis testing, regression, probability distributions, experimental design
• Ability to create clear visualizations that communicate insights to both technical and non-technical audiences
• Strong curiosity about failure modes and how data can predict failures
• Debugging mindset: investigate model errors and unexpected query results
• Eagerness to learn manufacturing, operations, and reliability engineering domains
• U.S. citizenship or other authorized status consistent with export-control and facility access requirements (see Additional Requirements)

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Preferred Skills & Experience

• Experience with machine learning in Python (scikit-learn): classification/regression, validation, cross-validation
• Familiarity with time-series analysis: trend plotting, change-point detection, smoothing noisy signals
• Exposure to dashboarding tools: Grafana, Tableau, Plotly Dash, or similar
• Understanding of basic reliability concepts: failure rates, survival curves, MTBF
• Prior internship or project experience analyzing real-world operational data (manufacturing, logistics, IoT sensors)
• Familiarity with version control (git) and collaborative analysis workflows
• Experience with data cleaning and wrangling: handling missing values, outlier detection, data quality assessment
• Exposure to anomaly detection techniques and statistical process control
• Familiarity with Jupyter notebooks and reproducible analysis practices

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Compensation & Location

• This role is posted for Denver, CO.
• Base salary (for Denver location): $75,000 per year.
• Employment type: Contract (3 months) — full-time hours; potential to convert to direct hire based on performance and business need.
• Work location: successful candidates should be located near Denver or Colorado Springs. This position observes a hybrid work environment with some on-site work required.

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Additional Requirements & Notes

• To conform with U.S. government space technology export regulations (including ITAR) and facility access needs, candidates must be a U.S. citizen, lawful permanent resident, protected individual as defined by 8 U.S.C. 1324b(a)(3), or otherwise eligible to obtain required authorizations from the U.S. Department of State.
• The role may involve typical laboratory/manufacturing working conditions (temperature variations, moderate noise) and routine physical demands such as sitting, standing, and occasional lifting. Specific physical requirements will be provided during the interview process or upon request.
• Confidential Client is committed to reasonable accommodations. If you require an accommodation during the application or interview process, please note that in your application materials.

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Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.