Data Scientist, AI/ML

Responsibilities: Analyze chaos engineering datasets to identify failure patterns and build ML models for automated failure detection and remediation. Develop scalable data pipelines and integrate AI-driven analysis capabilities into the core Gremlin product.

Requirements: Requires 5+ years of professional experience productionizing ML for distributed systems or DevOps, with expertise in causal inference or graph ML. Candidates must be skilled in building data pipelines and collaborating with SREs in an agile environment.

Key skills: Machine Learning, Causal Inference, Graph ML, Time-Series Modeling, Reinforcement Learning, Data Pipelines, Feature Stores, Distributed Systems, MLOps, Chaos Engineering, SRE, Software Development, Model Evaluation, Agile Development, Agentic AI, Failure Analysis

Keywords: AI/ML, Chaos Engineering, Distributed Systems, SRE, DevOps, Causal AI, Agentic Systems, MLOps, Feature Store, Model Serving, Root Cause Analysis, Blast Radius, Time-Series Modeling, Reinforcement Learning, Graph ML, Python, Infrastructure, Reliability Engineering, Agile, Data Pipelines