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About This Role
At Warner Music Group, we’re a global collective of music makers and music lovers, tech innovators and inspired entrepreneurs, game\-changing creatives and passionate team members. Here, we turn dreams into stardom and audiences into fans. We are guided by three core values that underpin everything we do across all our diverse businesses:
- Curiosity: We do our best work when we’re immersing ourselves in culture and breaking through barriers. Curiosity is the driving force behind creativity and ingenuity. It fuels innovation, and innovation is the key to our future.
- Collaboration: Making music and bringing it to the world is all about the power of originality amplified by teamwork. A great idea, like a great song, travels globally. We ignite passions and build connections across our diverse community of artists, songwriters, partners, and fans.
- Commitment: We pursue excellence for our team and our talent. Everything in music starts with a leap into the unknown, and we’re committed to keeping the faith, acting with integrity, and delivering on our promises.
WMG is home to a wide range of artists, musicians, and songwriters that fuel our success. That is why we are committed to creating a work environment that actively values, appreciates, and respects everyone. We encourage applications from people with a wide variety of backgrounds and experiences.
Consider a career at WMG and get the best of both worlds – an innovative global music company that retains the creative spirit of a nimble independent.
WMG’s Strategic Finance group works to influence WMG’s strategy and day\-to\-day operations by developing thoughtful insights; intelligence from its core business practices. The role involves the Data Scientist partnering with various departments across Warner Music’s Central Functions and Recorded Music.
Music Publishing Divisions to understand the trends and dynamics that drive the constant evolution of our business. Our goal for this role is to strengthen our analytical power and bring improved rigor to the insights we deliver to our family of labels and operators. We work with partners across the company to establish standard methodologies for breaking artists and sustaining catalog repertoire across consumption platforms, gauge the impact of marketing initiatives and campaigns, and work to understand user behavior around the world.
Intelligence fuels today’s music business. This role offers the chance to apply creativity and problem\-solving skills. You will assist our labels and artists in connecting with consumers across diverse media and mediums. The Strategic Finance team works closely with departments across the organization.
This role at the company offers a chance to observe how an international music organization operates and makes decisions. It also allows you to influence those decisions with careful intelligence work. As a collaborative team, we love learning new things, sharing ideas, thinking about the future of the music industry – and helping our company get there.
Here you’ll get to:
- Lead initiatives involving data analysis from inception to completion on topics such as revenue forecasts and subscriber consumption, digital advertising forecasts, global trends analysis, business performance trends, and more.
- Apply advanced quantitative modeling and data examination methods to address complex business challenges. Develop and maintain production\-level models, ensuring accuracy and efficiency.
- Work closely with Strategy, Corporate Finance, and operators to
understand business needs, and provide actionable insights to a diverse audience across the business.
- Proactively identify areas where data science can add new value by experimenting with different hypotheses and techniques to improve existing processes and techniques within the BI team.
- Apply AI, advanced tools and frameworks to support data examination, modeling, and visualization.
- Promote and participate in a culture of continuous learning within the team by providing feedback, sharing knowledge, and mentoring more junior team members.
About you:
- Bachelor’s, Master’s, or PhD in Economics, Econometrics, Statistics, Data Science, Applied Mathematics, or a related field.
- 3\-5 years of full\-time post\-graduation experience applying econometric and statistical methods in a business setting, preferably within the music, entertainment, or digital media industry.
- Proven experience working with and analyzing macroeconomic and industry\-level data from globally\-trusted third\-party sources like the UN, IMF, OECD, and World Bank.
- Proven capability to independently manage and carry out analytical projects involving rigorous statistical and econometric analysis.
- Demonstrated ability to lead and execute data science projects with minimal supervision.
- Proficiency in programming languages such as Python or R for data analysis and model development.
- Experience with machine learning frameworks and statistical tools.
- Strong understanding of SQL and working with large datasets.
- Familiarity with data visualization tools like Tableau or Power BI.
Expertise in fundamental data analysis, machine learning, and statistical modeling techniques.
- Ability to quickly learn and adapt to new techniques and technologies.
- Excellent communication skills, with the ability to explain complex concepts to non\-technical stakeholders.
- Strong problem\-solving abilities, with a bias towards action and a proactive approach to identifying opportunities and challenges.
We’d love it if you also had:
- Familiarity with the streaming music landscape or other digital media.
- Experience building analytical products and dashboards within BI software such as Tableau or alternative platforms like Streamlit.
As the home to 10K Projects, Asylum, Atlantic Music Group, East West, FFRR, Fueled by Ramen, Nonesuch, Parlophone, Rhino, Roadrunner, Sire, Warner Records, Warner Classics, and several other of the world’s premier recording labels, Warner Music Group champions emerging artists and global superstars alike. And our renowned publishing company, Warner Chappell Music, represents genre\-spanning songwriters and producers through a catalog of more than one million copyrights worldwide. Warner Music Group is also home to ADA, which supports the independent community, as well as artist services division WMX. In addition, WMG counts film and television storytelling powerhouse Warner Music Entertainment among its many brands.
Together, we are Warner Music Group: Independent Minds. Major Sound.
Love this job and want to apply?
Click the “Apply” link at the top of the page, or apply directly with your LinkedIn. Applying with LinkedIn will import all of the information you put in your profile, but will still allow you to upload a resume and cover letter.
Don’t be discouraged if you don’t hear from us right away. We’re taking our time to review all resumes, and to find the best people for WMG.
Thanks for your interest in working for WMG. We love it here, and think you will, too.
This position requires a minimum of 4 days per week in the office. We value in\-office collaboration, which is essential for talent development and strong working relationships. \#LI\-Onsite Salary Range: $115,000 \- $130,000 Annually Salary ranges are included for job postings where required by law. The actual base pay is dependent upon many factors, such as work experience and business needs. The pay range is subject to change at any time dependent on a variety of internal and external factors.
Warner Music Group is an Equal Opportunity Employer.
Links to relevant documents:
2026 Benefits At A Glance final.pdf
EVerify Participation Poster.pdf
Right To Work .pdf
Salary Context
This $115K-$130K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Warner Music Group, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($122K) sits 36% below the category median. Disclosed range: $115K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Warner Music Group AI Hiring
Warner Music Group has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $130K - $130K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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