What You’ll Do
The Senior Product Analyst is a key player in the Product department and helps drive data-driven decisions across different product domains. The Senior Product Analyst role includes bridging our data science endeavors with operational excellence, guiding the development of ML/AI technologies that power our classification systems querying and analyzing data, evaluating feature alternatives, designing experiments, and planning and creating dashboards. The individual in this role will work directly with Product Managers to support product decision questions and analyses.
This role owns the Classification & Brand Safety analytics domain for Open Web — the content ontology, site/page classification pipelines, and brand-safety suitability metrics that underpin our brand safety product suite. It also carries an explicit mandate to grow the team's analytics engineering maturity and to help design the next generation of AI-assisted / agentic analytics workflows that are replacing purely manual, reactive reporting and support.
This role is part of a professional Product Analysis group supporting core products serving advertisers and publishers across the digital landscape. The role requires big data analysis, attention to detail, business understanding, and strong product intuition. Communication skills are incredibly important, as the position interfaces across functions and geographies.
Job Responsibilities
Gathering and analyzing large amounts of information to discover & develop compelling, insightful, data-driven recommendations.
Communicating state of business, experiment results, etc. to product, data science and engineering teams.
Defining and monitoring key product metrics & system health, conducting root cause analyses.
Asking business and product-relevant questions and answering them with data.
Gaining a deep understanding of the products and their development processes, determining how to solve everyday issues best, and suggesting new features to solve ongoing issues.
Supporting and extending internal AI-driven recommendation systems for brand safety insights ; building the underlying data foundations, decision matrices, and parameterized SQL libraries that power agentic/conversational reporting tools, and helping identify and mitigate failure modes (e.g., hallucinations and AI Slop) in these agentic flows.
Building, testing, and maintaining analytics-engineering-grade data pipelines.
Leading or supporting high-visibility client escalations and QBRs with the Operations team (e.g., major advertiser recall/precision reviews), translating methodology into clear, defensible findings for external and internal stakeholders.
Who You Are
BA/BSc degree.
5+ years of experience with quantitative analysis.
Extensive hands-on experience with SQL
Hands-on experience with Python (for data manipulation), Excellent understanding of statistics (e.g., hypothesis testing, regression types).
Hands-on experience with, or strong motivation to develop, analytics engineering skills — building and maintaining DBT models (or equivalent), including staging/intermediate/mart layering, tests, and documentation.
Capable of working simultaneously on multiple tasks.
Comfort working with large-scale cloud data warehouses (Databricks, BigQuery, Snowflake) and BI/semantic layers (Looker/LookML).
Working familiarity with AI/LLM-assisted analytics workflows — e.g., using AI coding assistants (Claude Code, Cursor) for query and pipeline development, and a basic understanding of the difference between deterministic metric logic and probabilistic agentic systems, including their failure modes (hallucinations, prompt drift etc.)
Excellent verbal and written communication skills in English.
Preferred Qualifications
BA/BSc in a quantitative field: Statistics / Mathematics/ Economics /Computer Science, Physics/ Biology/ Psychology/ Chemistry/ Engineering, or other technical fields.
MA in a quantitative field
Hands-on dbt project experience (models, tests, macros, CI/CD via GitLab) and familiarity with analytics engineering best practices (semantic layer design, gold-table modeling, data quality/QA frameworks).
Experience designing or contributing to agentic/AI-assisted reporting tools (e.g., data contracts for LLM-driven report or recommendation generation, prompt design, retrieval pipelines).
Experience with Git-based version control and CI/CD for analytics code (GitLab CI/CD or similar).
