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com/careers ). Ssr. Data Scientist The Data Scientist will help develop and maintain our data infrastructure and visualization layers for digital marketing use cases, working closely with our Analytics, Data Science, and Solutions Engineering teams.

This role bridges the gap between raw data and decision-ready insights, building robust pipelines and interactive dashboards to measure media performance. The candidate can expect to participate in all technical phases of data engineering and client projects, including data discovery, pipeline construction, warehouse modeling, and reporting. It is also expected that they will participate in project planning, task estimation, and making data-driven recommendations for clients.

Responsibilities

Backend Data Infrastructure Build and maintain scheduled ingestion pipelines pulling spend, impression, and conversion data from media platform APIs (Google Ads, YouTube, Meta, TikTok, LinkedIn) and first-party sources into BigQuery. Design and own the BigQuery warehouse layer (schema, partitioning and clustering, incremental models) so modeling and measurement datasets are reproducible and query-efficient. Develop transformation logic that cleans, normalizes, and joins cross-platform data into modeling-ready tables for Meridian (MMM) and geo/matched-market test designs.

Implement data quality and validation (schema validation, freshness checks, anomaly detection) with alerting so failures and drift are caught before they reach downstream analysis. Stand up and maintain Data Manager API and GA4-to-BigQuery integrations to strengthen the first-party signal feeding the measurement stack. Manage the supporting GCP infrastructure (scheduling, service accounts, access controls, cost monitoring) and document lineage and runbooks so the work transfers cleanly off a single owner.

Frontend, Data Delivery Visualization Build and maintain reporting dashboards (Looker Studio, Looker, GA4 Omni Insights) that surface media performance, measurement results, and test readouts for client and internal audiences. Develop the serving and visualization layer that turns MMM, incrementality, and geo-test outputs into decision-ready views. Implement parameterized, self-serve filtering (campaign, platform, DMA, date range) so stakeholders pull what they need without ad hoc requests.

Own serving-layer performance and definitional consistency so dashboards refresh on cadence and reconcile to the warehouse as the single source of truth. Client Cross-functional Collaboration Nurture client understanding of the importance of building testing data-driven strategies. Utilize data visualization techniques to explain data models and pipelines to clients and internal teams.

Act as a consultative resource to help clients understand and integrate their internal data sources and testing processes.

Qualifications

Educational background in Computer Systems, Mathematics, Statistics, Physics, or related fields. Proven experience articulating, translating, and solving business problems through data and analytics engineering. Work experience with GCP (Google Cloud Platform), specifically BigQuery, Cloud Composer/Airflow, and GCP security/access controls.

2+ years of experience in data engineering, analytics engineering, or data warehousing. ). Proficiency in SQL and python data ecosystem for data manipulation and transformation.