Data Analytics Co-op: Build & Interpret Data Models

Rochester Regional Health

United States

Hybrid

USD 23,000 - 32,000

Full time

7 days ago
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Job summary

Rochester Regional Health is seeking a data-driven co-op to work with analysts and engineers on data models that power key business decisions. This hands-on role welcomes learning new tools and communicating insights clearly in a hybrid Rochester setting.

You'll gain experience with SQL, Python, R and BI tools while supporting KPI development and data quality initiatives in a collaborative environment.

Qualifications

  • Must be a student enrolled at RIT to participate in the co-op.
  • Background in Computer Science, Data Science, Statistics, or Business Analytics.
  • Comfort learning new technologies and platforms on the fly.
  • Familiarity with SQL, Python, or R; coursework counts.

Responsibilities

  • Assist in designing, building, and maintaining data models for reporting and analytics.
  • Explore datasets to identify trends, anomalies, and insights.
  • Collaborate with cross-functional teams to define data needs and communicate findings.
  • Document data pipelines and model logic for team reference.
  • Learn and apply new data tools as projects evolve.
  • Work with Microsoft Fabric and OneLake to manage data assets.
  • Support KPI development and tracking to inform decisions.
  • Contribute to data quality initiatives including validation and cleansing.
  • Present findings to both technical and non-technical stakeholders.

Skills

SQL
Python
R
Data analysis
Data visualization

Education

Computer Science
Data Science
Statistics
Business Analytics

Tools

Tableau
Power BI
Looker

Job description

Rochester Regional Health is seeking a data-driven co-op to work with analysts and engineers on data models that power key business decisions. This hands-on role welcomes learning new tools and communicating insights clearly in a hybrid Rochester setting.

You'll gain experience with SQL, Python, R and BI tools while supporting KPI development and data quality initiatives in a collaborative environment.

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