Analytics Scientist

Who You Are 

An Analytics Scientist with 3+ years of experience who can own the analytics work for a particular area of the business: designing data models, defining complex KPIs, building insightful dashboards, and leading advanced analytics that translate into relevant business decisions. A trusted partner to Product and Engineering that brings strong statistical judgment and clear communication.

Qualifications & Core Competencies

• Experience: 3+ years in data analytics roles. 
• Analytical ownership: Demonstrated ability to turn ambiguous questions into structured analyses and actionable recommendations that influence decisions.
• SQL & data quality: Advanced SQL proficiency to extract, validate, and shape data for analysis and reporting; strong discipline around data quality checks.
• BI & storytelling: Experience building decision-oriented dashboards in BI tools (e.g., Looker, Tableau, Power BI), with strong narrative and usability.
• Metrics & modeling: Experience defining and documenting complex KPIs and working with semantic/modeling layers (e.g., LookML/table modeling).
• Advanced analytics: Ability to apply advanced analytics to diagnose performance drivers, classify segments, quantify sensitivity to changes (e.g., pricing/incentives), understand unit-level economics, forecast key metrics, and analyze user journeys.
• Statistical fundamentals: Comfortable interpreting common statistical tests, developing simple models (t-tests, chi-square, regression/logit), and doing A/B testing.
• Data tools: 
  • Experience working with either Python or R for advanced analytics.
  • Familiarity applying ETL fundamentals to collaborate with Data Engineers.
• Communication & collaboration: Strong written and verbal communication; effective partner to Product and Engineering, able to present clearly to senior stakeholders.
• Education: Bachelor’s degree in a quantitative field (Economics, Statistics, Computer Science, Engineering, Actuarial Science, or similar); a Master’s degree is a plus.
• Language: Excellent oral and written communication in English.

Nice-to-Have

• Experience with Databricks/Snowflake or similar cloud data platforms.
• Familiarity with Git/version control and analytics engineering practices (tests, code review).
• Prior experience in experimentation platforms, event instrumentation, or product analytics frameworks.
• Experience applying A/B tests and causal inference models (DiD, matching, IV) to real-world problems, even if not used daily.

What You Will Do 

• Data Modeling & Metrics
  • Build and maintain semantic/data models to power consistent reporting.
  • Define complex KPIs and maintain documentation.
• Stakeholder Management & Collaboration
  • Be the main point of contact for a specific team, owning the end-to-end analytics loop from request intake to recommendation and implementation.
  • Continuous alignment on definitions, timelines, and tradeoffs.
  • Communicate complex ideas and bring disruptive innovation.
• Dashboards & Decision Support
  • Develop and iterate on interactive dashboards that highlight performance, drivers, and tradeoffs.
  • Establish monitoring views/alerts (where applicable) to detect anomalies and changes in key metrics.
• Advanced Analytics & Insights
  • Lead deep-dives to uncover root causes, quantify opportunities, and propose actions.
  • Turn ambiguous questions into structured analytical plans (assumptions, segmentation, validation steps).
• Experimentation (A/B testing)
  • Partner with PM/Eng to define hypotheses, success metrics, and guardrails.
  • Set up analysis frameworks for test readouts; escalate anomalies or instrumentation issues.

In a nutshell, you’ll be known for:

• Being the go-to analytics partner for your squad—turning ambiguity into clear plans and decisions.
• Raising the bar on metric quality and documentation so stakeholders can maintain trust in the numbers.
• Shipping analyses that actually define priorities and improve outcomes.
• Communicating complex findings in a clear, simple, and elegant manner, with clear tradeoffs and recommendations.
• Operating with a strong ownership mindset, proposing your own ideas to improve business outcomes.