✦ Luna Orbit — Data & Analytics

Data Scientist

at Asana

📍 San Francisco Onsite Posted March 19, 2026
Type Full-Time
Experience mid
Exp. Years 5+ years
Education Not specified
Category Data & Analytics

This role focuses on applying data science and machine learning techniques, especially large language models, to enhance user onboarding and growth strategies at a leading productivity software company.

  • Define growth strategies
  • Implement LLM-based onboarding
  • Conduct experiments
  • Analyze causal relationships
  • Drive data storytelling

Involves data analysis, ML modeling, LLM API integration, experimentation, and causal inference to optimize user engagement and product growth.

The ideal candidate is a mid-level data scientist with 5+ years of experience in machine learning, large language models, and user onboarding strategies. They are skilled in experimentation, causal inference, and storytelling with data to drive growth and product insights.

Data ScienceMachine LearningLarge Language ModelsUser onboardingGrowth strategies
AI/ML productsLLM APIsCustomer acquisitionData experimentationCausal inference
ChatGPTClaudeGeminiLLM APIs
Data ScienceMachine LearningLarge Language ModelsLLM APIsexperimentationcausal inferencestatistical techniquesdata storytellinguser onboardinggrowth strategies
Data ScienceMachine LearningLarge Language ModelsLLM APIsExperimentationCausal inferenceStatistical techniquesData storytellingUser onboardingGrowth strategies
Analytical thinkingCollaborationCommunicationProblem-solvingAdaptability
Industry Technology
Job Function Leverage data science and ML to improve user onboarding and growth
Role Subtype Data Scientist
Tech Domains AI & Machine Learning, Data & Analytics
Data ScienceMachine LearningLarge Language ModelsLLM APIsExperimentationCausal inferenceStatistical techniquesData storytellingUser onboardingGrowth strategiesAI/ML productsChatGPTClaudeGeminiData experimentationCustomer acquisition

Less than 5 years of experience, No experience with large language models, Lack of expertise in data storytelling, Inability to work onsite in San Francisco

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