Principal Analytics & AI Engineer
Tasks
- Architect data models and pipelines with data engineering
- Build analytical models and conduct explainability trust and governance for AI driven analytics
- Build model ready datasets and feature pipelines
- Create semantic layers metric frameworks and analytical abstractions
- Design build and certify reusable AI powered self service metrics dashboards and agents
- Develop predictive diagnostic and causal models for product analytics
- Implement MLOps for reproducible production machine learning
- Implement experimentation strategy with A B testing quasi experiments and causal inference
- Lead complex cross functional analytics projects
- Mentor lead and evangelize analytics and ML AI best practices
- Perform scenario modeling and simulation
- Translate product questions into formal models and statistically sound analyses
- Validate models and review analytical rigor
Perks/Benefits
- 401k matching
- Flexible paid time off
- Paid Holidays
- Paid events and team activities
- Paid parental leave
- Work from home options
Skills/Tech-stack
A/B | A/B Testing | Artificial Intelligence | B testing | BigQuery | Causal Inference | Causal modeling | DBT | Data Modeling | Experimental Design | Experimentation | Feature Engineering | Language Processing | MLOps | Machine Learning | Natural Language | Natural Language Processing | Predictive Modeling | Python | Quasi Experimental | Quasi-Experimental Design | SQL | Tableau
Education
Regions
Countries
States
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