GN - Data Scientist - 217

Job Description

We are looking for a Data Scientist to design, build, and own actionable insights derived from high-volume user interaction data. In this role, you will work with time series and streaming data generated from user events (such as mouse clicks) and screen recordings to define performance metrics, detect abnormal behavior, and support data-driven decision making.

You will take ownership of open-ended and ambiguous problems, translating raw and often noisy data into meaningful signals. Working closely with engineering and product teams, you will help evaluate detected issues, understand their root causes, and propose practical recommendations to improve product reliability and developer experience.

Key Responsibilities

• Analyze large-scale time series and event-based data from user interactions
• Structure ambiguous problems and translate them into measurable signals and metrics
• Define high-level performance metrics, including normal ranges, limits, and thresholds
• Design and implement anomaly detection methods for individual users and user groups
• Build, validate, and backtest insights and alerts using historical and near-real-time data
• Operate effectively with incomplete, noisy, or delayed data in real-world environments
• Combine user interaction data with screen or video metadata to enrich insights
• Propose data-driven actions or recommendations when abnormal behavior is detected
• Collaborate with software engineers to integrate insights into developer-facing products
• Clearly document and explain metrics, insights, and alert logic to technical audiences

Requirements

• Strong foundation in data science fundamentals (statistics, data analysis, metrics)
• Hands-on experience with time series analysis
• Experience analyzing user behavior or event-based data
• Solid understanding of anomaly detection techniques
• Proficiency in Python and SQL
• Experience working with large-scale or high-frequency datasets
• Ability to think in terms of product impact and actionable insights
• Strong communication skills and experience working with engineering teams
  

Nice to Have

• Experience with streaming data platforms (Kafka, Spark, Flink, or similar)
• Familiarity with real-time analytics or monitoring systems
• Basic experience with video or screen data processing or metadata analysis
• Experience defining or maintaining alerting systems
• Background in developer-facing products or internal tooling
  

What Success Looks Like

• Metrics accurately describe normal versus abnormal behavior
• Alerts are meaningful, explainable, and have low false-positive rates
• Insights are trusted and actively used by developers to improve the product
• Data science solutions are reliable, testable, and production-ready