Data Scientist, Finance Data & Insights
Job description
About the role
This position partners with Legal and Finance to expand data capabilities across Spotify's analytics environment. The role focuses on operational support, automation, and guidance for complex financial and legal initiatives. You will translate intricate regulatory and financial requirements into structured analytical workflows. The position requires deep collaboration with cross-functional stakeholders to ensure data integrity and decision readiness. You will own the design of data structures that support finance and legal reporting needs. The role emphasizes turning ad hoc requests into reusable analytical assets and clear documentation. You will guide stakeholders in interpreting results and building a data-driven culture around legal and financial topics. Success in this role is measured by the reliability, clarity, and scalability of the solutions you deliver.
Key facts
This role is based in New York, with 2-3 in-person days per week requested.
Flexibility exists for remote work, while specific in-person meetings are still required.
What you'll do
Drive the development of data solutions that address finance and legal requirements across Spotify's analytics landscape.
Design and maintain datasets that provide clear visibility into legal and financial metrics for non-technical stakeholders.
Partner with Finance and Legal teams to translate complex requirements into analytical specifications and data models.
Automate manual reporting processes to reduce effort, increase consistency, and improve timeliness of financial insights.
Develop and document methodologies that ensure transparency, reproducibility, and compliance with internal controls.
Lead data quality initiatives that support accurate financial close cycles and regulatory reporting needs.
Implement monitoring and alerting for key data pipelines to ensure stakeholders receive timely and accurate information.
Support experimentation and metric definition efforts that help the business understand the financial impact of product and policy changes.
Create dashboards and data products that enable Finance and Legal teams to track performance and risk indicators.
Champion best practices in data management, documentation, and governance across analytics projects.
Collaborate with data engineers to optimize data pipelines that serve finance, legal, and compliance use cases.
Translate business questions into testable hypotheses and analytical approaches that drive informed decision-making.
Contribute to the development of tools and frameworks that enable scalable self-service analytics for finance stakeholders.
Act as a technical advisor for data-related initiatives that intersect with legal, financial, and operational domains.
Requirements
Bachelor's or Master's degree in information systems, computer science, finance, mathematics, or another quantitative discipline.
4+ years of experience in data analysis, data science, or a related role within finance, legal, or a highly regulated environment.
Strong proficiency in SQL for querying large datasets and performing complex joins, aggregations, and window calculations.
Expert-level ability in a programming language such as Python for data manipulation, analysis, and automation.
Solid understanding of statistical concepts including descriptive statistics, hypothesis testing, and regression analysis.
Experience working with financial, legal, or compliance data is essential for this role.
Demonstrated skill in translating business requirements into structured data models and analytical workflows.
Proven ability to communicate technical concepts to non-technical stakeholders through clear documentation and presentation.
Nice to have
Experience with data visualization tools such as Tableau or Looker for building stakeholder-facing dashboards.
Familiarity with machine learning techniques applied to finance or legal risk problems.
Knowledge of data governance, privacy, or regulatory frameworks relevant to financial services.
Experience building self-service analytics platforms or data products for internal stakeholders.
Background in audio, media, or digital content industries to understand Spotify-specific context.
Practical notes
This role is based in New York, with 2-3 in-person days per week requested.
Flexibility exists for remote work, while specific in-person meetings are still required.
Typical interview steps include a SQL or coding exercise, a statistics question, and a case study.
Candidates may be asked to design a metric, interpret an experiment, or build a small model.
Some companies give a take-home analysis.
Expect questions about past projects and the business impact of your work.
Interviewers often evaluate how you communicate uncertainty and business impact, not only the math.
Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Data Scientists commonly work with SQL, Python, and modern AI tools to turn raw data into decisions.
Domain expertise in finance or legal data helps analysts support specialized stakeholders and regulatory needs.
Automation and self-service reporting rely on data pipelines and clear process documentation.
Cross-functional collaboration drives product and operational outcomes in data-intensive environments.
Questions to ask
Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year.
Asking what past hires did well is a strong final question.
Keep the list short and pick the questions that matter most to you.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead.
Many professionals specialize in machine learning, analytics, or infrastructure.
Cross-functional work with product and engineering teams becomes more important at senior levels.
The field changes quickly, so continuous learning is part of the job.
Professionals who can translate numbers into decisions tend to advance fastest.
About the company
Spotify is the world's most popular audio streaming subscription service with over 600 million users.
Founded by Daniel Ek and Martin Lorentzon in 2006, Spotify went public via direct listing on the NYSE in April 2018.
The platform hosts over 100 million tracks and 6 million podcasts.