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Data Scientist – Music Mission

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Remote from
USA
Salary
USD 116,994–167,135 / yr
Employment
Full Time
Experience
Senior
Published
Apply before
2 Nov 2026
Listing views
37
Application actions
2
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AI Summary

The role, at a glance.

Spotify is seeking a Data Scientist to serve as the analytical lead for the Discovery Mode ML squad within its Music Mission. The role evaluates and improves measurement and campaign-optimization models through experimentation, model-performance analysis, KPI design, and dashboards. It partners closely with ML engineers, product managers, and business stakeholders to translate statistical findings into product decisions that improve artist and label outcomes. Candidates need 4+ years of data science experience, strong Python and SQL skills, and experience with A/B testing, causal inference, recommendation systems, or advertising measurement.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior-level product data science role requiring rigorous experimentation, ML evaluation, and the ability to influence model and product decisions. The analytical lead is expected to independently frame ambiguous questions while communicating complex results to technical and non-technical partners.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$142,065
US market range$115k–$175k
AI insightThe disclosed annual US base-salary range is $116,994 to $167,135 USD, with a midpoint of $142,064.50. This is competitive with the estimated US market range of $115,000 to $175,000 for an experienced product-focused data scientist specializing in experimentation, machine-learning evaluation, and recommendation or advertising measurement; equity may provide additional compensation.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you evaluate whether a new Discovery Mode model improves outcomes for artists and listeners?

I would begin by defining success metrics across model quality, listener engagement, artist outcomes, and potential guardrails such as user satisfaction or recommendation diversity. Where feasible, I would run a randomized controlled experiment, validate assignment and data quality, estimate treatment effects with confidence intervals, and segment results to identify heterogeneous impact. I would combine the experimental result with operational model-health metrics before recommending rollout.

Describe how you would design an A/B test for a recommendation-related product change.

I would first define the decision the experiment must support and select a randomization unit that limits interference, such as listener or artist where appropriate. I would specify a primary metric, guardrail metrics, expected effect size, sample-size requirements, and analysis plan before launch. After validating exposure and instrumentation, I would analyze both aggregate and segment-level results, account for novelty effects and multiple comparisons, and clearly state limitations.

What metrics would you put on a dashboard for a production measurement model?

I would include data freshness and pipeline health, prediction distributions, calibration and error metrics, drift indicators, coverage, and model latency or reliability. I would pair these with product measures such as campaign participation, customer outcomes, engagement, and business impact. The dashboard would use thresholds and alerts so the squad can quickly distinguish data-quality issues from meaningful model degradation.

How do you explain a statistically complex finding to a non-technical product stakeholder?

I start with the decision and user impact rather than the methodology. For example, I would say that the test indicates the feature likely improved artist reach by a defined range while showing no meaningful negative listener-engagement effect, then explain the confidence and key caveats in plain language. I use a concise visual and offer technical detail separately for stakeholders who need it.

Tell us how you would partner with ML engineers when model performance and customer outcomes disagree.

I would jointly investigate whether the offline metric is aligned with the actual product objective, checking data quality, calibration, cohort performance, and production behavior. If the model improves an offline score but not customer outcomes, I would recommend revisiting the evaluation objective and running targeted experiments or error analyses. The goal is to create shared evidence and optimize for durable customer impact rather than a single technical metric.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

The Music Mission enables music creators to grow, engage, and monetize their fan bases on Spotify. Central to the Music Mission’s vision is the development of promotional tools for artists and label teams, powered by Spotify’s deep knowledge of listener behavior. Products like Discovery Mode, Marquee, Showcase, Music Videos, and Clips help artists and their teams grow their audiences, connect with fans, and achieve their goals on Spotify.

We’re looking for a Data Scientist to join Discovery Mode within the Music Mission. Discovery Mode is a tool for artists and music marketers designed to help find new listeners when it matters most. With Discovery Mode, artists and labels identify songs that are a priority, and our systems use that signal to inform the algorithms that power personalized recommendations. This role sits within the ML squad that builds and operates the models behind Discovery Mode’s measurement system, and you’ll serve as the squad’s analytical lead.

In this role, you’ll partner closely with product managers and ML engineers to evaluate and improve the models that power Discovery Mode. You’ll tackle complex analytical problems by designing experiments, developing evaluation frameworks, and building the analytical foundations that help keep our models accurate, reliable, and impactful for artists. As part of the Product Insights team within Music Mission, you’ll help shape the measurement systems behind one of Spotify’s most important promotion products.

What You’ll Do

  • Own the analytical function for the Discovery Mode ML squad, driving evaluation and continuous improvement of the models that power measurement and campaign optimization
  • Partner with ML engineers to develop evaluation frameworks and identify opportunities to improve model performance, reliability, and customer impact
  • Design and execute rigorous experiments to evaluate model quality, measure outcomes, and guide model development
  • Conduct deep-dive analyses to assess model performance and translate findings into clear, actionable recommendations for product and business stakeholders
  • Build, maintain, and evolve dashboards that track model health, customer metrics, and program performance
  • Collaborate with product managers, engineers, and cross-functional partners to align analytical priorities with squad goals and customer needs
  • Contribute to the broader Product Insights community by sharing best practices and helping raise the bar for analytics across Discovery Mode

Who You Are

  • You have 4+ years of experience in a data science role and a degree in data science, statistics, economics, mathematics, or a related quantitative field
  • You have experience measuring customer outcomes, defining KPIs, and connecting analytical insights to product decisions
  • You know how to design and implement A/B tests, understand when experimentation is the right tool, and interpret results with appropriate rigor
  • You have experience evaluating machine learning model performance and partnering with ML engineers to improve model and customer outcomes
  • You are comfortable working in a highly technical environment and collaborating closely with engineering partners
  • You communicate complex statistical concepts clearly to both technical and non-technical audiences
  • You have strong data science fundamentals, including Python, SQL, BigQuery, dbt, data storytelling, and experience working within cross-functional product teams
  • You have experience in areas such as advertising measurement, recommendation systems, experimentation, or causal inference at scale

Where You’ll Be

  • We offer you the flexibility to work where you work best! For this role, you can be within the EST timezone region as long as we have a work location.
  • This team operates within the Eastern Standard time zone for collaboration.

Compensation

Additional Information

The United States base range for this position is $116,994 – $167,135 USD, 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, paid flexible holidays, and paid sick leave. These ranges may be modified in the future.

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know – we’re here to support you in any way we can.

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