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Remote Data Science Music Jobs in Michigan (NOW HIRING)

Bachelor's degree in Data Science, Engineering, Mathematics, Computer Science, Operations Research ... Benefit Summary This role is remote but if you live within 50 miles within Dearborn, MI, you will ...

Data Engineer

Wyoming, MI ยท On-site +1

$103K - $124K/yr

... Data Science, Information Systems, or a related field. * (Required) Strong SQL skills and ... Flexible/remote work options.

Director of Data Intelligence | Remote | Michigan or Minnesota Preferred Role Snapshot: * Set the ... Build and lead a high-performing team of data scientists, analysts, and engineers. * Promote a ...

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Remote Data Science Music information

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or data. Data scientists often focus on the most impactful features or data subsets to optimize model performance and efficiency.

How does a remote data science role in the music industry typically collaborate with other departments, such as marketing or A&R?

In a remote data science music role, collaboration with teams like marketing, product, and A&R (Artists & Repertoire) is often achieved through regular virtual meetings, shared analytics dashboards, and cross-functional project management tools. Data scientists may analyze listener trends, predict song success, or segment audiences, providing actionable insights to guide marketing campaigns and artist development strategies. Strong communication skills and proactive coordination are essential, as data-driven recommendations directly inform creative and business decisions within the company.

What are the key skills and qualifications needed to thrive as a Remote Data Science Music professional, and why are they important?

To thrive as a Remote Data Science Music professional, you need strong skills in statistics, machine learning, and music theory, often supported by a degree in data science, computer science, or music technology. Familiarity with programming languages like Python or R, experience with audio analysis tools, and proficiency in music-specific data platforms are typically required. Creativity, problem-solving, and effective remote communication are crucial soft skills for success in collaborative and innovative projects. These skills enable the effective analysis of music data, drive innovation in music technology, and foster productive teamwork in a remote environment.

What is the difference between Remote Data Science Music vs Remote Data Analysis?

AspectRemote Data Science MusicRemote Data Analysis
Required CredentialsBachelor's/Master's in Data Science, Statistics, or related fields; programming skills in Python/R; knowledge of music dataBachelor's in Data Analysis, Statistics, or related; proficiency in Excel, SQL, and visualization tools
Work EnvironmentCollaborative teams, often in tech or entertainment industries, with a focus on music dataBusiness or research settings analyzing various data types, often in finance, marketing, or healthcare
Employer & Industry UsageMusic tech companies, streaming services, entertainment industryCorporate, research institutions, marketing agencies across multiple industries

Remote Data Science Music involves applying data science skills specifically to music-related data, often requiring knowledge of music industry trends and audio data analysis. Remote Data Analysis is broader, focusing on analyzing various data types across industries. While both roles require strong analytical skills and familiarity with data tools, Remote Data Science Music emphasizes music-specific data and industry knowledge.

What is a Remote Data Science Music job?

A Remote Data Science Music job involves using data analysis, machine learning, and statistical techniques to analyze or generate music-related data, all while working remotely. Professionals in this field may work with streaming data, user preferences, music recommendation systems, audio signal analysis, or music composition algorithms. They typically collaborate with music platforms, record labels, or research teams to uncover trends, improve recommendations, or create new music experiences. This role requires both data science skills and an understanding of music theory or the music industry.

Is 30 too late for data science?

Remote data science roles are accessible at any age, including at 30, as the field values skills like programming, statistics, and machine learning. Many professionals transition into data science later in their careers, and continuous learning through online courses and certifications can help build relevant expertise regardless of age.

Can data science jobs be done remotely?

Data science jobs, including roles in remote data science music, are often performed remotely as they primarily involve analyzing data, coding, and using tools like Python or R. Many companies offer remote positions for data scientists, especially with skills in cloud computing and collaboration platforms, making remote work a common option in the field.

How much does a Sony music data analyst make?

A data analyst working in the music industry, such as at Sony Music, typically earns between $60,000 and $90,000 annually, depending on experience, location, and skill level. Proficiency in data analysis tools like SQL, Python, or R, and understanding of music industry metrics, can influence salary levels.
What are the most commonly searched types of Data Science Music jobs in Michigan? The most popular types of Data Science Music jobs in Michigan are:
What cities in Michigan are hiring for Remote Data Science Music jobs? Cities in Michigan with the most Remote Data Science Music job openings:
Infographic showing various Remote Data Science Music job openings in Michigan as of June 2026, with employment types broken down into 75% Full Time, and 25% Part Time. Highlights an 100% Remote job distribution.

Principal Data Scientist (Remote)

Accident Fund Holdings, Inc.

Lansing, MI โ€ข On-site, Remote

Full-time

Posted 9 days ago


Job description


SUMMARY
AF Group is seeking a Principal Data Scientist with expertise in either Commercial Property or Personal Homeowners insurance to serve as an individual contributor and technical authority on applying advanced analytics and machine learning to complex business problems, including pricing, risk selection, and other underwriting challenges. This role owns the end-to-end analytical lifecycle, from problem formulation and model development through deployment, monitoring, and governance. Partners closely with Actuarial, MLOps, and IT to deliver scalable, production-ready solutions. The Principal Data Scientist ensures long-term model performance through rigorous validation, drift monitoring, and audit-ready documentation, while advancing analytical best practices and evaluating emerging techniques relevant to commercial P&C insurance.
RESPONSIBILITIES/TASKS:
  • Acquires, organizes, and cleanses structured and unstructured data.
  • Conducts in-depth analysis to uncover trends, risks, and business opportunities.
  • Applies statistical modeling, machine learning, and advanced analytics to develop predictive and prescriptive solutions.
  • Evaluate solution performance using statistically rigorous methods and measure the impact to business outcomes.
  • Collaborate with MLOps and IT partners to transition solution prototypes from pilot validation into production environments.
  • Ensures ongoing model health through post-deployment monitoring, drift detection, and audit-compliant governance practices.
  • Creates and communicates results to senior level audiences of varying backgrounds, using business-facing presentations, reports, and dashboards.
  • Author and maintain comprehensive technical documentation for data lineage, codebases, results, and production changes.
  • Provides technical and project guidance, including peer review of work, for data science team.
  • Leads the evaluation of new analytic tools and processes.
  • Drives investigation and adoption of advanced machine learning and AI innovations.

EDUCATION:
Bachelor's Degree in Data Science, Statistics, Mathematics, Operations Research, Actuarial Science, Computer Science, Engineering, Physics or related technical field required. Advanced degree preferred.
EXPERIENCE:
10 years of experience in data science or related advanced analytics domains, including research and teaching, with 3+ years of technical leadership.
REQUIRED SKILLS/KNOWLEDGE/ABILITIES
  • 3+ years of experience supporting underwriting functions, including loss modeling, for Commercial Property (preferred) or Personal Homeowners insurance.
  • Demonstrated expertise using Poisson, Gamma, and Tweedie distributions to build loss ratio, pure premium, and frequency-severity loss models for pricing.
  • Extensive experience leveraging supervised learning models (e.g., XGBoost, GLM, etc.) and unsupervised techniques (e.g., K-means, PCA, etc.) to solve complex data science problems.
  • Advanced Python programming skills supporting data science, including scikit-learn and pandas.
  • Proficient data wrangling and ETL abilities using SQL on relational databases.
  • Comfortable explaining machine learning models with partial dependence plots and SHAP values.
  • Ability to conduct experiments e.g., A/B Testing, to evaluate the causal impact of model-driven decisions.
  • Experience using version control tools such as Git and Azure DevOps.
  • Experience working in cloud computing environments such as Azure, AWS, GCP, etc.

PREFERRED SKILLS/KNOWLEDGE/ABILITIES
  • Experience supporting at least one other commercial or personal line outside of Property lines.
  • In-depth understanding of General Liability (aka Casualty), Workers Compensation, or Commercial Vehicle insurance.
  • Knowledge of actuarial concepts and terminology used in pricing and ratemaking.
  • Experience with Claims, Marketing, or Operations functions within P&C insurance settings.
  • Ability to develop Agentic AI solutions to drive autonomous decision-making and task orchestration.
  • Familiarity with causal modeling techniques such as Meta-learners, Causal Forest, Double ML, etc.
  • Knowledge of advanced neural net architectures like LSTM, CNN, Transformers, Graph NN, etc.
  • Understanding of NLP concepts such as topic modeling, Word2Vec, sentiment analysis, OCR, etc.
  • Experience programming in the R language.
  • Ability to build interactive dashboards using frameworks such as Plotly Dash, Power BI, Flask, etc.

ADDITIONAL INFORMATION:
The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. This job description does not constitute a contract for employment.
PAY RANGE:
"Actual compensation decision relies on the consideration of internal equity, candidate's skills and professional experience, geographic location, market and other potential factors. It is not standard practice for an offer to be at or near the top of the range, and therefore a reasonable estimate for this role is between $137,900 and $231,000."
We are an Equal Opportunity Employer. We will not tolerate discrimination or harassment in any form. Candidates for the position stated above are hired on an "at will" basis. Nothing herein is intended to create a contract.
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