Key skills
SQLPythonProgrammatic advertisingA/B testingCausal inference methodsMachine learningBigQueryData pipeline developmentAIMachine LearningMLA/B Testing
About this role
Allstate is a leading insurance company that has been innovating for over 90 years to meet the evolving needs of its customers. The Senior Business Data Analyst for Ad Operations is responsible for monitoring and optimizing digital advertising campaigns, collaborating with cross-functional teams to drive insights and implement data-driven solutions. This role involves leveraging data analysis to enhance campaign performance and contribute to automated decision-making systems.
Responsibilities:
- Monitor and optimize programmatic campaigns using real-time and historical data signals
- Diagnose performance issues across supply, demand, and delivery systems; recommend targeted interventions
- Design and evaluate A/B and multivariate experiments, integrating sensor-based features into test frameworks
- Collaborate cross-functionally with AdOps, Product, and Data Science to operationalize optimization strategies at scale
- Contribute to the development of automated decisioning systems for campaign management (e.g., budget allocation, bidding adjustments)
- Partner with data scientists to translate business problems into ML-driven solutions (e.g., propensity models, bidding optimization, segmentation)
- Drive the evolution from manual campaign management to automated, intelligence-driven optimization systems
Requirements:
- Bachelor's degree
- 4+ years of experience in data analysis, campaign optimization, or programmatic advertising
- Strong proficiency in SQL and Python for large-scale data analysis
- Solid understanding of programmatic advertising ecosystem and KPIs (CPM, CPC, win rate, CPA, ROAS)
- Experience designing and analyzing experiments (A/B testing, causal inference methods)
- Strong problem-solving and analytical thinking skills with a focus on driving measurable outcomes
- Ability to communicate insights effectively to both technical and non-technical stakeholders
- Familiarity with machine learning concepts and collaborating on production ML systems
- Experience with cloud data platforms (e.g., BigQuery) and data pipeline development
- Experience building semantic layers or analytical models for scalable reporting
- Experience leveraging AI-powered coding assistants to enhance productivity