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Data Scientist - Matching

Nashville, Tennessee, United States

 

Are you interested in joining a purpose-driven company in the music industry? Do you thrive in a collaborative, hybrid work environment? If you do, we would like to get to know you.

 

WORKING AT THE MLC

 

The MLC is committed to excellence, service, transparency, and diversity. Our culture is collaborative, and our team works in a hybrid environment. On our team, you are respected, valued for your unique strengths and experiences, and empowered to identify and resolve your own challenges.

 

THE ROLE

 

As a Data Scientist, you will design and implement machine learning models and statistical methodologies to solve the challenge of matching digital usage reports from DSPs (Digital Service Providers) to our repertoire metadata. You will work on integrating and analyzing outputs from multiple sources — including recording-to-work matches, recording clustering, content type classification, and matching suggestions — to produce high-confidence matching decisions.  The Matching Platform is a critical tool that enables The MLC to drive accurate royalty payments to our Members.

 

QUALIFICATIONS

 

  • Minimum five (5) years’ experience in a Data Scientist or Machine Learning Engineer role.
  • Strong expertise in statistical modeling, data manipulation, and machine learning, including model development, evaluation, and deployment.
  • Experience working with large and varied datasets from multiple external sources.
  • Proficiency in Python and data science libraries (Pandas, scikit-learn, TensorFlow, PyTorch).
  • Solid understanding of data engineering principles and ability to collaborate closely with data platform engineers. Experience working with AWS. Experience with Dagster is a plus.
  • Excellent collaboration and communication skills, with the ability to clearly present technical findings to both technical and non-technical audiences.

 

ESSENTIAL RESPONSIBILITIES

 

AS A TEAM MANAGER YOU WILL:

  • Design, build, and evaluate machine learning models for metadata matching and decisioning.
  • Develop methodologies to integrate outputs from multiple external vendor systems into unified, high-confidence match decisions.
  • Create and maintain datasets and features required for modeling, ensuring high data quality and consistency.
  • Analyze model performance, recommend improvements, and iterate based on real-world feedback.
  • Write clear and reproducible code, including unit tests and documentation.
  • Work closely with data platform engineers to ensure models are deployed reliably within the Matching Platform.
  • Contribute to the evolution of the Matching Platform’s technical strategy and roadmap.

 

AS A TECHNICAL LEAD YOU WILL

  • Work in an Agile environment and participate actively in team ceremonies and collaborative planning.
  • Foster constructive dialogue and seek resolution when confronted with technical challenges.
  • Uphold high standards of software and model development best practices.
  • Embrace a data-driven, collaborative, and continuous improvement mindset.

 

YOU WILL CHAMPION THE MLC’S CULTURE BY:

  • Applying The MLC’s Guiding Principles to your work and your behaviors
  • Being process-oriented, data-driven, and tech-savvy; being collaborative, curious, and open to new ideas
  • Engaging in a diverse and dynamic team; continuing with personal development
  • Inspiring others with your enthusiasm and humility

 

THE MLC IS AN EQUAL OPPORTUNITY EMPLOYER THAT COMMITS TO PURSUING, HIRING, AND CELEBRATING A DIVERSE WORKFORCE AND CREATING AN INCLUSIVE ENVIRONMENT. THE MLC DOES NOT MAKE EMPLOYMENT DECISIONS BASED ON RACE, COLOR, RELIGION OR RELIGIOUS BELIEF, ETHNIC OR NATIONAL ORIGIN, SEX, GENDER, GENDER-IDENTITY, SEXUAL ORIENTATION, MARITAL STATUS, CITIZENSHIP STATUS, DISABILITY, AGE, MILITARY OR VETERAN STATUS, OR ANY OTHER CATEGORY PROTECTED BY LOCAL, STATE, OR FEDERAL LAW. THIS POLICY APPLIES TO ALL TERMS AND CONDITIONS OF EMPLOYMENT, INCLUDING RECRUITING, HIRING, PLACEMENT, PROMOTION, TERMINATION, LAYOFF, TRANSFER, LEAVES OF ABSENCE, AND COMPENSATION.

 

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