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About This Role
Data, Research \& Insights
Permanent
New York
Our mission on the Advertising Product \& Technology team is to build a next generation advertising platform that aligns with our unique value proposition for audio and video. We work to scale the user experience for our fans and hundreds of thousands of advertisers. This scale brings unique challenges as well as tremendous opportunities for our artists and creators.
We are currently recruiting for a Data Scientist within the multidisciplinary Advertising Product Insights team. This role is focused on supporting the scaling of the future of Spotify Advertising, our self\-service advertising platform, Spotify Ads Manager. Our work sits at the intersection of R\&D and the Ads business, and we are responsible for bringing the right data and insights to our breadth of stakeholders to understand Spotify Ads Manager performance, and the advertiser experience with Spotify.
Our mission is to enable the product and business teams to meet their objectives through evidence based decision making and customer focus. As a data scientist in this group you will use a range of data science tools and capabilities to work closely with product, design, engineering, user research, product marketing, and our business stakeholders on one or more of the following areas:
Customer facing product development for advertisers
Data requirements and management for our ever expanding platform
Spotify Advertiser and Spotify Ads Manager growth initiatives
Experimentation, causal inference and Insights for Spotify Ads Manager optimization
Defining new metrics, forecasts, and benchmarks for product evolution
Evolving Ads Manager self\-service performance and UX reporting
What You'll Do
+ Perform analyses on large sets of data to extract practical insights that will help drive product, platform and business decisions throughout all stages of the product development lifecycle
+ Define data requirements and support the implementation of data specs for product launches
+ Support the management and maintenance of the Spotify Ads Manager’s data and reporting (including dashboards) ecosystem
+ Build lasting solutions to surface critical data and performance metrics
+ Design and conduct experiments to drive iterative improvement to both user and advertiser metrics through efficient experimentation
+ Communicate data\-driven insights and recommendations to key partners
+ Develop individually to continue to grow your impact, and help mentor peers and early career colleagues
Who You Are
+ You have 3\+ years experience in a similar data science role
+ You are interested in supporting the growth of a maturing platform, building the foundation for the future of Spotify Advertising
+ Ideally you have experience working in an analytics role that focuses on improving internal decision making while working with one of \- platform teams, product teams, sales teams or marketing teams
+ You have the technical competence to perform more advanced analytics: Coding skills for analytics and data extraction (SQL, Python \- packages such as pandas and numpy) and Data visualization (Tableau, Looker, matplotlib)
+ You have experience performing analysis with large datasets
+ You have a strong understanding of statistics and machine learning
+ You value cross\-disciplinary work and understand how to match qualitative research with your quantitative analysis
+ You are capable of solving very loosely defined problems
+ You are a communicative person that values building strong relationships with colleagues and collaborators and have the ability to explain complex topics in simple terms
+ You are curious, exhibit a growth mindset and are excited by working on a constantly evolving team
Where You'll Be
+ We are a distributed workforce enabling our band members to find a work mode that is best for them!
+ Where in the world? For this role, it can be anywhere in the North America region in which we have a work location
+ Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.
+ Working hours? We operate within the Eastern time zone for collaboration.
Learn about life at Spotify
The United States base range for this position is $107,766 \- $153,951, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.
Our global benefits
Extensive learning opportunities, through our dedicated team, GreenHouse.
Flexible share incentives letting you choose how you share in our success.
Global parental leave, six months off \- for all new parents.
All The Feels, our employee assistance program and self\-care hub.
Flexible public holidays, swap days off according to your values and beliefs.
Salary Context
This $107K-$153K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Spotify, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($130K) sits 36% below the category median. Disclosed range: $107K to $153K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Spotify AI Hiring
Spotify has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $153K - $262K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above the national median.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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