USASoftware Engineer I, Data Science (New Grad)
4 дн. назад
USAJunior
pythondata analysismachine learningpredictive modelingdashboard
Entry-level software engineer role focused on data science to analyze spacecraft data and build predictive models.
Обязанности
- 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
Требования
- Bachelor's or Master's degree in data science, statistics, industrial engineering, applied mathematics, operations research, 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)
- Work Location —Successful candidates will be located near Denver or Colorado Springs. While we observe a hybrid work environment, some work must be done on site.
- Work environment —the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
- Physical demands —the physical demands of the job, including bending, sitting, lifting and driving.
- This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite
- To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State.
- is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.
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Будет плюсом
- Experience with machine learning in Python: scikit-learn for classification/regression, model validation, train/test splits, cross-validation
- Familiarity with time-series analysis: plotting sensor trends, detecting change points, smoothing noisy signals
- Exposure to data visualization tools: Grafana, Tableau, Plotly Dash, or similar dashboard frameworks
- Understanding of basic reliability concepts: failure rates, survival curves, mean time between failures (MTBF)
- Prior internship or project analyzing real-world operational data: manufacturing, logistics, quality control, IoT sensor data
- Experience with version control (git) and collaborative data analysis workflows
- Coursework or projects in industrial engineering, operations research, or quality management
- Familiarity with data cleaning and wrangling: handling missing values, outlier detection, data quality assessment
- Understanding of experimental design: A/B testing, randomized controlled trials, confounding variables
- Exposure to anomaly detection techniques: z-scores, control charts, boxplot analysis
- Prior work with manufacturing or hardware production data (even from coursework or academic projects)
- Familiarity with Jupyter notebooks, literate programming, and reproducible analysis practices
Условия
- Base Salary: Denver: $75,000; Long Beach: $80,000
Другое
- Space is a warfighting domain. seeks those with the talent and ambition to build the technology that secures it.
- OUR MISSION
- 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.
- YOUR MISSION
- You'll turn spacecraft data into actionable insights across manufacturing and operations: building dashboards that surface production bottlenecks and on-orbit anomalies, analyzing test failures and mission telemetry to identify root causes, training predictive models that flag at-risk components before integration and detect spacecraft health degradation during missions, and mining telemetry to catch anomalies operators would miss. Your work spans the full spacecraft lifecycle. Pre-launch, you'll analyze manufacturing telemetry, test logs, failure reports, and supplier data to catch problems before integration. Post-launch, you'll monitor on-orbit telemetry streams, detect anomalies in spacecraft health data, analyze mission performance, and flag degradation patterns that predict future fa
- This is a 3 month temporary employment engagement. There is potential to convert to regular employment based on performance and business need.