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Data Engineering and Machine Learning Intern – Hybrid
Organize and standardize DISA Technologies’ mineral-processing test data for cloud analytics. Analyze historical records to assess data quality and identify machine-learning-ready relationships.
विवरण
- Catalog historical data sources with details such as location, client, project, site or program, equipment unit, date range, format, and completeness.
- Document specifications for recurring record formats and include representative files.
- Convert test files into a standardized tabular template and place them in the assigned staging area.
- Create data-quality reports that flag missing information, incomplete or questionable analyses, and inconsistencies across files.
- Explore datasets to uncover potential correlations and relationships.
- Prepare feasibility memos explaining which questions the historical data can and cannot answer.
- Standardize file names and tags in legacy folders when needed.
- Assemble metadata required for migration to a document-management system when needed.
- Match test records with related measurement datasets when needed.
आवश्यकताएँ
- Currently pursuing or recently completing a degree in computer science, data science, statistics, engineering, or a related discipline.
- Proficiency with Python and pandas, or an equivalent toolset, for reading, cleaning, and reshaping tabular data.
- Hands-on experience handling untidy real-world files, including Excel workbooks, CSVs, PDFs, and inconsistent naming or structures.
- Methodical and detail-oriented, with a willingness to record missing information and unresolved inconsistencies.
- Able to work independently from defined schemas and templates, while seeking clarification when files do not conform.
- Strong written communication skills.
- Familiarity with SQL, data catalogs, or data-lake concepts such as bronze, silver, and gold layers.
- Basic statistics or exploratory data-analysis experience using tools such as matplotlib, seaborn, or Jupyter.
- Interest in mineral processing, chemistry, or industrial process data; previous domain expertise is not required.
- Power-user familiarity with SharePoint or OneDrive.
लाभ
- A managed laptop or virtual desktop is provided.
- A confidentiality acknowledgment is required because the role involves confidential client data.
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