Senior Data Scientist

Responsibilities

Analytics & Modeling

• Independently design and execute complex analyses, statistical models, and measurement frameworks that inform product and business decisions.
• Apply statistical methods such as experimental design, causal inference, predictive modeling, and Bayesian approaches.
• Translate ambiguous business questions into structured analytical approaches, identifying appropriate metrics, methodologies, and data.
• Balance rigor, speed, precision, and practicality based on the business decision.

Business Impact & Experimentation

• Own analytics for a product or business area, defining and maintaining core KPIs and measurement frameworks.
• Identify opportunities where analytics and experimentation can improve user behavior, revenue, conversion, retention, cost efficiency, and other outcomes.
• Design and analyze experiments with Product and Engineering partners.
• Connect metric movements and analytical findings to underlying business drivers and recommended actions.
• Quantify the impact of product and business initiatives to influence roadmap and prioritization decisions.

Collaboration & Influence

• Partner with Product, Engineering, Marketing, and Data Product stakeholders on product and business decisions.
• Communicate complex analyses through clear narratives and visualizations, including assumptions, trade-offs, confidence levels, and expected business impact.
• Translate technical and analytical concepts for non-technical partners.
• Increase data literacy by making metrics, analyses, and recommendations accessible and actionable.
• Informally mentor junior data scientists and analysts.

Technical Excellence

• Use Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work.
• Apply strong practices in experimentation, model validation, reproducibility, and governance.
• Use AI/ML tools thoughtfully to improve analytical workflows, automation, documentation, and anomaly detection while maintaining appropriate validation.

Requirements

Minimum Requirements

• 5+ years of experience in data science, machine learning, or applied analytics, with ownership of complex analytical problems in product-driven environments.
• Strong expertise in statistical modeling, experimental design, and causal inference.
• Experience owning KPIs, measurement, or analytics for a product or business area and translating findings into actionable recommendations.
• Ability to structure ambiguous business problems and connect analytical findings to revenue, cost, conversion, retention, or user behavior.
• Strong proficiency in SQL and at least one programming language, preferably Python.
• Experience with large-scale data systems such as Snowflake, dbt, Airflow, or Spark.
• Ability to communicate complex technical insights, trade-offs, and confidence levels to technical and non-technical stakeholders.
• Ability to work independently while navigating cross-functional dependencies and escalating broader trade-offs appropriately.
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.

Preferred Requirements

• Advanced degree in a quantitative discipline.
• Experience deploying or operationalizing predictive or statistical models.
• Consumer-product experience using data to understand user behavior and inform engagement, retention, monetization, or other customer outcomes.
• Experience building frameworks that improve experimentation velocity.