Senior Data Scientist II

Advanced Analytics & Modeling

• Design, build, and deploy predictive and causal models that power personalization, retention, and monetization strategies.
• Apply advanced statistical methods, including Bayesian inference, causal impact analysis, and hierarchical modeling, to guide decision-making.
• Develop and operationalize experimentation and measurement frameworks that ensure accurate attribution and reproducibility across Fetch’s products.

Business Impact & Experimentation

• Quantify the impact of key business and product initiatives, translating complex model outputs into actionable insights.
• Design and analyze experiments that test hypotheses about user behavior, product features, and marketing initiatives.
• Establish metrics and analytical frameworks that measure Fetch’s progress toward strategic growth and retention goals.

Collaboration & Influence

• Partner closely with Product, Engineering, and Data Product teams to transform insights into scalable data products and intelligent systems.
• Communicate complex analyses through clear narratives and visualizations that drive executive understanding and action.
• Mentor and collaborate with peers to strengthen Fetch’s scientific rigor, data culture, and analytical storytelling capabilities.

Technical Excellence

• Leverage tools and technologies such as Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to build and scale robust data science solutions.
• Champion best practices in experimentation, model validation, reproducibility, and governance.
• Advance Fetch’s use of AI/ML tools for automation, documentation, and anomaly detection, emphasizing validation and responsible use.

• 8+ years of experience in data science, machine learning, or applied analytics with proven impact in product-driven environments.
• Deep expertise in statistical modeling, experimental design, and causal inference.
• Strong proficiency in SQL and at least one programming language (Python preferred).
• Experience working with large-scale data systems such as Snowflake, dbt, Airflow, or Spark.
• Proven ability to communicate complex technical insights to non-technical stakeholders and drive strategic decision-making.
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.

• Advanced degree (Master’s or Ph.D.) in a quantitative discipline.
• Experience deploying ML models into production and managing model lifecycle and performance.
• Background in consumer technology, ad tech, or personalization systems.
• Experience building frameworks that improve experimentation velocity and decision quality.
• Familiarity with privacy-preserving data modeling and compliance standards such as GDPR or CCPA.
• Mentorship experience or demonstrated leadership in scientific or analytical development.