Job Description
Role SummaryAs we scale our self-service analytics and DS capabilities, we're looking for a Principal Data Scientist, Analytics to develop scalable data & analytics solutions and framework, and intuitive tools that bring insights directly to business teams.
The Principal Data Scientist, Analytics plays a critical role in delivering trusted, scalable analytics solutions to stakeholders across the company, particularly in the Product & Engineering areas. Working closely with architects and data engineers, you'll help shape data models and pipelines and build Analytics / DS frameworks that serve as the foundation for high-impact dashboards and predictive and prescriptive analytics. You'll design user-centric tools that empower teams to explore data, gain insights, and make better decisions, powered by platforms like BigQuery, AI agents, and Tableau.
Key ResponsibilitiesSelf Service Analytics & Insights- Design, build, and certify reusable, self-service metrics, dashboards, skills and agents, and analytical products based on business needs for stakeholders in Product and Engineering teams to independently answer complex questions and provide actionable insights.
- Partner with Product and Engineering to align strategic goals and embed analytics and decision logic directly into the product lifecycle.
- Create semantic layers, metric frameworks, and analytical abstractions that power AI-assisted insights and natural language querying.
- Develop complex analytical models, provide "so-what" deep dive analyses, and present findings to leadership for high-impact use cases.
- Own explainability, trust, and governance for AI-driven analytics experiences.
Data Science, Modeling & Experimentation- Develop predictive, diagnostic, and causal models to understand and optimize product adoption, engagement, retention, and monetization.
- Translate ambiguous product questions into formal models and statistically sound analyses.
- Perform scenario modeling and simulation to inform roadmap and investment decisions.
- Own experimentation strategy, including A/B testing, quasi-experiments, and causal inference.
- Partner with data engineering to architect data stack, build model-ready datasets and scalable feature pipelines, and embed data operations for reproducible data science models.
- Validate models, create framework, review analytical rigor, and raise the quality bar across product analytics and data science work.
Leadership- Partner with Product, Engineering, and Design teams to define and prioritize roadmap
- Mentor and lead cross-functional efforts to evangelize analytical and DS/AI best practices both within and outside the organization.
- Lead complex cross-functional analytics projects.
Required Qualifications- MS or PhD in Computer Science, Statistics, Mathematics, or a related field.
- 10+ years of experience in Data Science and Analytics, with a track record of delivering high-impact product insights and statistical models at scale.
- Expert-level proficiency in Python and SQL; must be comfortable navigating massive datasets in cloud environments (e.g., BigQuery) and possess hands-on experience building complex DS models and utilizing LLM models.
- Demonstrated ability to build and scale AI-powered analytics, architect and manage a modern DS stack from the ground up.
- Deep understanding of Product Analytics metrics and concepts, ideally within a SaaS or Platform environment.
- Exceptional business acumen with the ability to translate vague product questions into concrete technical concepts and roadmap, and articulate business impact to non-technical executive stakeholders.
- Advanced experience partnering with data engineers and working with dbt or similar data modeling frameworks.
- Strong commitment to analytical rigor, reproducibility, and best practices in data science workflows.
- A history of leveling up mid-to-senior analysts and data scientists and driving analytics excellence across the organization.
The approximate annual base compensation range is $193,500 to $227,500. The actual offer, reflecting the total compensation package and benefits, will be determined by a number of factors including the applicant's experience, knowledge, skills, and abilities, geography, as well as internal equity among our team.
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