Operations Engineer Associate
Lockheed Martin
Camden, AR0–2 yrs2h agovia LinkedIn
This job at a glance
- Who can apply
- New grads and up to 2 years · Bachelor's degree
- Must know
- Data visualizationData analysisBusiness analysisPythonSQLPower BI
- Nice to have
- StatisticsMachine learning
- Work
- On-site in Camden
- Pay
- Not stated by the employer.
- Skill demand
- Python38%Data visualization15%Business analysis12%Data analysis8%of 99 similar entry-level jobs posted in the US this week
- Listed
- Nov 12, 2026 · still open
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About the Role
This role drives the producibility and quality of products and processes across engineering disciplines, utilizing a data-driven approach to transform shop-floor and program data into actionable reports and insights.
Key Responsibilities
- Analyze industrial engineering data (cycle times, throughput, yield, scrap, labor, capacity).
- Translate data into metrics, dashboards, and recommendations to drive cost-effective decisions.
- Partner with program owners to embed insights into daily decisions and surface risks early.
Requirements & Tech Stack
- Bachelor's in Industrial Engineering, Industrial and Systems Engineering, Computer Science, or Data Analytics.
- 0 to 2 years of experience (including internships, co-ops, or academic projects) in manufacturing, data engineering, or process improvement.
- Proficiency in data analysis (SQL, Python) and visualization (Power BI, Tableau, or Grafana), demonstrated through work or academic projects.
- Proficiency with Microsoft Office (Excel, Word, PowerPoint, Outlook), including Excel tools such as macros, PivotTables, and lookup functions (VLOOKUP/XLOOKUP).
Desired Skills
- Experience with R or other statistical languages.
- Familiarity building internal tools or automations that deliver analysis directly to end users, including AI/ML where it adds value.
- Ability to translate technical results into clear business recommendations.
- Detail-oriented, self-starter who thrives in a collaborative environment.
- Resilient, adaptable, and eager to learn in fast-paced environments.
- Interest in or exposure to the LM portfolio of products.
- Exposure to Lean Six Sigma principles.