1

Internship Data Science Music Jobs in Florida (NOW HIRING)

Data Science Associate (Governance) Company Overview At Mitsubishi Power, we're not just building better clean energy technologies; we're architecting a better future. Our team is boldly redefining ...

... data science, machine learning, or quantitative analytics experience. Relevant internship, co‑op, or graduate research may count toward experience. * Hands‑on experience with SQL on a modern ...

New

Data Scientist

Orlando, FL · On-site

$107K/yr

In this role, you will bridge the gap between theoretical data science and real-world operational ... Technical Leadership & Mentorship (10%) • Lead and manage a pipeline of talent, including interns ...

Skillbridge Internship -IO

Doral, FL

$14 - $18.50/hr

Your unit Commander must authorize participation prior to start of internship. Career tracks ... Data Science, Machine Learning, Programming Eligibility: * Has served at least 180 days on active ...

next page

Showing results 1-20

Internship Data Science Music information

What is an internship in data science for the music industry?

An internship in data science for the music industry is a temporary position where students or recent graduates work with music companies or organizations to apply data analysis techniques to music-related problems. Interns may analyze streaming data, build recommendation systems, or help understand listener behavior using machine learning and statistical methods. The goal is to gain practical experience in both data science and the unique challenges of the music sector. These internships often involve working with large datasets, coding, and presenting insights to stakeholders. They can be a great way to start a career at the intersection of technology and music.

What types of projects can I expect to work on during a data science internship in the music industry?

As a Data Science intern in the music industry, you’ll typically work on projects involving the analysis of streaming data, user behavior, and recommendation systems. You may assist with developing tools to predict song popularity, analyze listening habits, segment user audiences, or help refine algorithms that personalize playlists. These projects often require collaboration with product managers, engineers, and music curators, offering valuable exposure to both technical and creative problem-solving in a fast-paced, data-driven environment.

What are the key skills and qualifications needed to thrive as an internship data science music, and why are they important?

To thrive as a Data Science Music Intern, you need foundational knowledge in statistics, programming (Python or R), and basic understanding of music theory or audio analysis, often supported by ongoing studies in data science, computer science, or a related field. Familiarity with data analysis tools like Pandas, NumPy, Jupyter, and music-specific libraries such as librosa, as well as experience with SQL and Git, is highly valued. Strong problem-solving skills, creativity, and effective communication help interns collaborate on innovative projects and present complex findings clearly. These abilities are essential for extracting insights from music data, contributing to team goals, and advancing both technical and creative aspects of music data projects.

What is the difference between Internship Data Science Music vs Data Analyst Music?

AspectInternship Data Science MusicData Analyst Music
Required CredentialsRelevant coursework, basic programming skillsBachelor's in Data Science, Statistics, or related field
Work EnvironmentInternship setting, entry-level projectsFull-time or part-time roles in music industry companies
Industry UsageUsed for training and skill development in music techAnalyzing music consumption, sales, and trends
Search & Comparison IntentUnderstanding internship opportunities in music data scienceComparing roles for career progression in music data analysis

Internship Data Science Music typically involves entry-level training and project work in music-related data science, often as a temporary position. Data Analyst Music is a full-time role focused on analyzing music industry data to inform business decisions. While both roles require analytical skills, internships are more about learning, whereas data analyst positions involve ongoing responsibilities in the music industry.

What are the most commonly searched types of Data Science Music jobs in Florida? The most popular types of Data Science Music jobs in Florida are:
Infographic showing various Internship Data Science Music job openings in Florida as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 9% Part Time, 1% Temporary, and 8% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution.

Data Science Internship (Data Platforms)

MHI

Lake Mary, FL • On-site

Internship

Re-posted 2 days ago


Job description

Data Science Associate
Company Overview
At Mitsubishi Power, we're not just building better clean energy technologies; we're architecting a better future. Our team is boldly redefining power generation to accelerate the world's energy transition. We operate as one team, pushing toward our vision of the future. We value problem solvers, prioritize collaboration, and support each other in an inclusive culture built on accountability and authenticity by demonstrating our values: Safety, Family, Innovative, Inclusive, Accountable & Courageous. Together, we're building the future we all aspire to - making net zero a reality.
Role Overview
The Data Scientist Intern supports Mitsubishi Power's IT Data Platforms team by contributing to data quality, data cataloging, and automation efforts within enterprise data environments. This hands-on internship provides practical experience working with cloud-based data platforms, centralized data repositories, and Microsoft Power Platform tools. The role collaborates with Data Platforms leadership, Enterprise Applications, and IT stakeholders to support scalable, high-quality data solutions used across the business.
Key Responsibilities
  • Assist with data quality assessments across enterprise data sources, identifying gaps, inconsistencies, and improvement opportunities.
  • Support data catalog activities including documentation of datasets, metadata, data definitions, and lineage.
  • Validate, organize, and prepare ingested data within centralized platforms such as data lakes and structured repositories.
  • Contribute to automation efforts to surface data into Microsoft Power Apps and Power Automate workflows.
  • Perform data validation and reconciliation to ensure accuracy and completeness between source and ingested data.
  • Assist with development of basic dashboards, reports, and visualizations to support data visibility and usage.
  • Support testing and user acceptance activities to validate data processes and automation solutions.
  • Maintain tracking artifacts such as data quality logs, catalog trackers, and automation inventories.
  • Document data processes, standards, and learnings to support long-term platform sustainability.

Requirements
  • Assist with data quality assessments across enterprise data sources, identifying gaps, inconsistencies, and improvement opportunities.
  • Support data catalog activities including documentation of datasets, metadata, data definitions, and lineage.
  • Validate, organize, and prepare ingested data within centralized platforms such as data lakes and structured repositories.
  • Contribute to automation efforts to surface data into Microsoft Power Apps and Power Automate workflows.
  • Perform data validation and reconciliation to ensure accuracy and completeness between source and ingested data.
  • Assist with development of basic dashboards, reports, and visualizations to support data visibility and usage.
  • Support testing and user acceptance activities to validate data processes and automation solutions.
  • Maintain tracking artifacts such as data quality logs, catalog trackers, and automation inventories.
  • Document data processes, standards, and learnings to support long-term platform sustainability.

Learning Outcomes
  • Gain hands-on experience with enterprise data platforms, including data lakes and cloud-based environments, understanding how data supports business operations.
  • Develop core data skills in data quality, validation, cataloging, and metadata management using real-world datasets.
  • Apply analytics and automation tools such as Excel, SQL, and Microsoft Power Platform to support business processes and workflows.
  • Strengthen problem-solving and communication skills by working cross-functionally and translating data insights into clear, actionable outcomes.

Mitsubishi Power is an Equal Employment Opportunity (EEO) employer actively seeking to diversify the workforce and is committed to a policy of equal employment opportunity. Therefore, all qualified applicants regardless of race, color, religion, gender, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally recognized protected basis under applicable law, are strongly encouraged to apply.