Data Engineering, ML Intern

DISA Technologies, Inc.

. Build a complete inventory of historical data sources, including location, client, project, site/program, equipment unit, date range, format, and completeness .

Posted 10/3/2026contractCasper • Wyoming • United StatesEntry Level💰 $20 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Proficient in data analysis and management, with strong capabilities in Python and pandas for data manipulation. Experienced in producing data quality reports and conducting exploratory data analysis to derive insights from complex datasets.

Highest-signal resume keywords

Python ProgrammingData Cleaning and ReshapingExploratory Data AnalysisSQL ExposureMetadata Preparation

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data AnalysisData Quality ReportingStatistical AnalysisFile Naming ConventionsTabular Data Management

Soft Skills

Clear Written CommunicationMethodical ApproachIndependent Work

Tools & Technologies

PandasExcelCSVPDFJupyter

Industry Keywords

Data CatalogsData LakeBronze-Silver-Gold ConceptsMineral ProcessingChemistry

Tech Stack

Tools & technologies

PandasPythonSQL

About the role

Key responsibilities & impact

Build a complete inventory of historical data sources, including location, client, project, site/program, equipment unit, date range, format, and completeness
Write format specifications for common record types, with example files
Transcribe test files into a consistent tabular template and save them to a designated staging area
Produce data quality reports covering gaps, incomplete or suspect analyses, and contradictions between files
Conduct exploratory data analysis to identify candidate correlations across datasets
Write feasibility memos on which questions the historical data can and cannot support
Apply file naming/tagging conventions to legacy folders as needed
Prepare metadata for a document-system migration as needed
Cross-reference test records against related measurement datasets as needed

Requirements

What you’ll need

Currently enrolled in or recently graduated from a program in computer science, data science, statistics, engineering, or a related field
Comfortable with Python and pandas (or equivalent) for reading, cleaning, and reshaping tabular data
Experience working with messy real-world files — Excel workbooks, CSVs, PDFs, inconsistent naming and structure
Careful and methodical; willing to document what you find, including what's missing or doesn't add up
Able to work independently from a defined schema and templates, and to ask for clarification when a file doesn't fit them
Clear written communication
Exposure to SQL, data catalogs, or data lake / "bronze-silver-gold" concepts
Basic statistics or EDA experience (matplotlib/seaborn, Jupyter)
Interest in mineral processing, chemistry, or industrial process data. No prior domain knowledge required
Familiarity with SharePoint/OneDrive at a power-user level

Benefits

Comp & perks

Managed laptop or virtual desktop
Confidentiality acknowledgment required due to handling confidential client data