• Analyze Gremlin’s proprietary dataset of millions of chaos engineering experiments to identify failure patterns, root causes, and resilience signals across complex distributed systems • Pretraining and fine-tuning machine learning models that automatically detect, classify, and explain failures observed during chaos experiments • Build intelligent systems that deliver automated remediation recommendations, and eventually orchestration, by learning from historical experiment outcomes and system behavior • Develop scalable data pipelines and feature stores to process, enrich, and serve large volumes of experiment data for both model training and real-time inference • Collaborate closely with platform engineers and SREs to integrate AI-driven failure analysis and remediation capabilities directly into Gremlin’s core product • Apply advanced techniques, including causal inference, graph ML, time-series modeling, and reinforcement learning, to continuously improve the accuracy and actionability of automated failure analysis • Translate insights from millions of chaos experiments into AI-powered features that help customers automatically understand blast radius, pinpoint root causes, and accelerate recovery • Research and productionize novel ML approaches, including causal AI and agentic systems, that turn raw chaos experiment data into automated, reliable remediation strategies

We are allies of the physician throughout their entire journey and we are looking for people who identify with our purpose, values and culture to join our team. Let’s transform healthcare together with those who have medicine as a vocation! This is an open position in the Data area, to work remotely. As a Mid-level Data Analyst at Afya, your primary responsibility will be to perform data analysis and modeling, and to develop dashboards and metrics in Power BI, ensuring the quality, clarity and governance of information.

• Own the strategic direction of the device data and performance platform • Define how Palmetto connects with hardware manufacturers through integration partnerships • Shape data models for performance monitoring and alerting • Build workflows for field service and asset management teams • Collaborate across Asset Management, Field Service, Operations, Customer Support, Finance, and Engineering • Drive alerting logic and performance insights • Ensure monitoring coverage with new asset types • Own product strategy, OKRs, roadmap, and quarterly planning • Encourage AI use for discovery and learning • Manage stakeholder expectations with clear communication

• Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. • Develop contextualized feature sets using specific domain knowledge in soccer. • Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. • Strive to constantly improve model performance using insights from rigorous offline and online experimentation. • Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. • Adhere to software engineering best practices and contribute to shared code repositories. • Document modeling work and present to stakeholders and other technical and non-technical partners.