🛠 Tech Stack

💼 About This Role

You'll bridge complex datasets and strategic action by synthesizing information from external sources like Google Search Console and internal data lakes to provide leadership with clarity for confident decisions. You will inform Wikimedia's strategy for ensuring content discoverability on Wikipedia and other projects. This role offers the chance to shape high-level negotiations around LLM content usage and drive grassroots donor growth.

🎯 What You'll Do

• Integrate fragmented data from internal data lake, BigQuery, Matomo, and external tools like Ahrefs and Semrush.
• Translate analytics and trends by geography, project, and topic into actionable narratives for leadership.
• Develop automated systems to detect under-indexed content and gaps in search results.
• Establish metrics to track content usage in LLMs and generative search engines.

📋 Requirements

• Fluent in Python and Jupyter for data analysis and modeling.
• Fluent in SQL and working with large-scale data using Hive, Presto, Druid, or Spark.
• Experience building dashboards/reports with tools like Jupyter, Quarto, or Superset.
• Bachelor's + 5 years or Master's + 3 years related experience or equivalent.

✨ Nice to Have

• Familiarity with Wikimedia projects, moderation processes, and communities.
• Excellent communication skills with statistical nuance for experiment design.
• Comfortable in a highly collaborative, remote environment.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Phone Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min