Create Music Group is an LA-based digital music company that provides artists and labels with distribution, rights management, and creative services. We monetize over 12 billion monthly music streams and have collected over $150 Million in previously unclaimed revenue for artists and labels. Clients include Future, JLo, City Girls, Marshmello, Don Diablo, Trippie Redd, Migos, Universal Music Group, and more.
Label Engine was founded in 2008 with a single purpose: to reduce the number of time-consuming tasks involved in growing and operating a successful record label. Since being acquired by Create Music Group in 2015, LE has gained a reputation as one of the most efficient distribution platforms in the industry and has become a household name among artists, managers, and label owners worldwide. By combining a unique set of essential tools into one easy-to-use system, Label Engine takes the hassle out of distribution and lets you focus on what matters most: your music.

JOB SUMMARY

As a Senior Data Scientist, you will lead initiatives to deliver actionable analytics that support acquisitions and cross-functional data solutions. This strategic role offers the opportunity to directly influence business outcomes and enhance our data products. You’ll be part of a collaborative team of engineers, analysts, product managers, and company leaders that leverages data to drive strategic decisions and strengthen our data product capabilities.

QUALIFICATIONS

• 5+ years of experience as a Data Scientist, with a proven track record of delivering impactful models in music, entertainment, or a data-intensive industry
• Advanced proficiency in Python, R, or similar programming languages, with expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Deep understanding of predictive modeling, statistical analysis, and advanced techniques, including regression, clustering, and time-series forecasting
• Demonstrated experience with APIs (e.g., Viberate) for data extraction and analysis, along with proficiency in managing data pipelines (e.g., BigQuery, Airflow)
• Strong data engineering skills, with the ability to handle large, complex datasets and optimize for performance
• Exceptional communication skills, capable of presenting complex models and insights to non-technical stakeholders, including executive leadership
• A strategic thinker with a problem-solving mindset, comfortable working in a fast-paced environment with shifting priorities
• Preferred: Direct experience with music data (e.g., streams, playlists, social metrics) or supporting M&A initiatives; familiarity with Looker or AI Platform is an advantage
• Ideal: Exposure to advanced analytics methods, such as NLP, graph theory, or recommendation systems, demonstrating an ability to innovate

• Develop and lead the creation of predictive models to identify trends in our music catalog, focusing on metrics such as streams, playlist performance, follower growth, and release impact
• Oversee the integration of internal Spotify data and external Viberate data to train models, ensuring robust and scalable data pipelines
• Rigorously test and validate models to ensure their accuracy and reliability, enabling precise M&A decisions, such as identifying high-value catalog acquisitions
• Expand model capabilities to analyze non-internal music, supporting broader acquisition strategies
• Collaborate with the business to translate model insights into strategic data driven recommendations
• Design and implement cross-functional dashboards that deliver actionable insights to product, A&R, and marketing teams, enhancing the utility of our data product
• Drive innovation in music analytics by exploring advanced techniques, such as natural language processing for social sentiment analysis or graph-based models for artist networks
• Partner with engineers, analysts, and product managers to elevate our data capabilities, providing mentorship to junior team members as the team grows
• Navigate complex datasets, adapt to evolving priorities, and deliver insights that influence high-stakes business decisions