Data Engineering & ML Intern

DISA Uranium•Casper, WY

About The Position

DISA Technologies develops HPSA (High Pressure Slurry Attrition) mineral processing technology. Over several years of lab, pilot, and field testing we've accumulated a large body of test data, including feed characterization, particle size, chemistry, and process performance. This data is spread across many projects, sites, and file formats. Before we can use it in analytics and machine learning, it needs to be found, catalogued, and put into a consistent format. Together with the ML Engineer, you'll own the consolidation of our historical test data end-to-end, working from an inventory schema, format specification template, and tabular data template we provide. The goal is a governed, catalogued set of clean data files ready to load into our cloud data platform.

Requirements

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

Nice To Haves

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

Responsibilities

Build a complete inventory of historical data sources: location, client, project, site/program, equipment unit, date range, format, and completeness — extending and correcting an existing inventory rather than starting from scratch
Write a short format specification for the common record types you encounter, with example files for each
Transcribe test files into a consistent tabular template and save them to a designated staging area
Produce a data quality report covering gaps, incomplete or suspect analyses, and contradictions between files
After the core work: exploratory data analysis to identify candidate correlations across datasets (e.g., feed characteristics vs. process performance) and short feasibility memos on which questions the historical data can and can't support
Possible additional work depending on progress: applying file naming/tagging conventions to legacy folders, metadata preparation for a document-system migration, cross-referencing test records against related measurement datasets