Analytics Engineer I (Entry-Level) (3897)

Navarro Research and Engineering is recruiting an Analytics Engineer I (Entry-Level) (3897) in Oak Ridge, TN. 

Navarro Research & Engineering is an award-winning federal contractor dedicated to partnering with clients to advance clean energy and deliver effective solutions for complex challenges in the nuclear and environmental fields. Joining Navarro means being a part of an exceptional team committed to quality and safety while also looking for innovative strategies to create value for the client’s success. Headquartered in Oak Ridge, Tennessee, Navarro has active programs in place across the nation for DOE/NNSA, NASA, and the Department of Defense. 

Position Summary 

The Analytics Engineer I (Entry-Level) will support Navarro’s Data & Analytics team by helping build and maintain data pipelines, assisting with reporting and dashboarding efforts, and contributing to applied AI projects. This role is well suited for an early-career professional interested in growing into a strong analytics or applied AI contributor, including work involving AI/ML techniques to extract, structure, and validate key information from unstructured documents. 

Responsibilities 

• Assist in designing, building, and maintaining data pipelines and ETL/ELT processes. 

• Write and optimize SQL queries to extract, transform, and analyze data from multiple sources. 

• Support the development of dashboards and reports using Excel and other business intelligence tools. 

• Use Python and/or R to automate data workflows, clean datasets, and perform exploratory analysis. 

• Build and support AI/ML-driven workflows for extracting key text and structured data from documents, including NLP, LLM-based extraction, and OCR plus ML pipelines. 

• Collaborate with data scientists, analysts, and business stakeholders to understand data and applied AI project needs. 

• Help document data models, pipeline logic, and AI/ML workflow processes. 

• Contribute to data quality checks and validation processes for both traditional data and AI-generated outputs. 

Requirements

Requirements 

• Bachelor’s degree in Data Analytics, Statistics, Computer Science, Mathematics, Engineering, or a related field. 

• Working knowledge of SQL for querying and manipulating data. 

• Proficiency in Excel, including formulas, pivot tables, and data visualization. 

• Proficiency in Python and/or R, including experience with relevant ML/AI libraries such as pandas, scikit-learn, or similar tools. 

• Hands-on exposure to AI/ML concepts through coursework, projects, or internships, such as text extraction, natural language processing, or document processing. 

• Strong analytical and problem-solving skills. 

• Good communication skills and the ability to work in a collaborative, cross-functional environment. 

• Attention to detail and a commitment to data accuracy. 

Preferred Qualifications 

• Master’s degree in a relevant quantitative field. 

• Experience with cloud platforms such as Azure, AWS, or Google Cloud and BI tools such as Tableau, Power BI, or Looker. 

• Familiarity with version control, such as Git, and basic software engineering practices. 

• Experience with LLM-based tools or APIs for document parsing, text extraction, entity recognition, or information extraction. 

• Experience with OCR tools or unstructured document processing, including PDF or text parsing libraries
• Local candidates are preferred.  

Due to the nature of the government contract requirements and/or clearances requirements, US citizenship is required.  

Navarro is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, race, religion, color, national origin, age, disability, veteran’s status, or any classification protected by applicable state or local law.  

EEO Employer/Vet/Disabled 

Benefits

• Health Care Plan (Medical, Dental & Vision)
• Retirement Plan (401k)
• Life Insurance (Basic, Voluntary & AD&D)
• Paid Time Off (Vacation & Public Holidays)
• Short Term & Long Term Disability