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Madhuri Naik
Mid-level Data Scientist specializing in predictive analytics and LLM-powered data pipelines
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Early-career engineer from BNP Paribas who drove a large-scale observability modernization—selecting and implementing Prometheus/Grafana for a 2000+ server estate, then productionizing it on Kubernetes via Docker/Jenkins. Known for hands-on demos, strong documentation/templates, and pragmatic troubleshooting (including custom Python metrics) that improved visibility and cut debugging time by ~60%.
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Key Strengths
- Led migration from legacy monitoring to Prometheus/Grafana across 2000+ servers
- Scaled observability stack to production using Docker + Kubernetes deployed via Jenkins
- Performance/resource optimization by tuning pod CPU/memory and reducing scrape intervals/metrics
- Built custom monitoring metrics using Python when PromQL/platform limitations arose
- Implemented proactive alerting (threshold + email) to enable real-time incident response
- Reduced debugging time ~60% by improving alert content/log visibility
- Drove adoption through tailored demos, follow-along sessions, and reusable documentation/templates
- Partnered with Learning & Development to create a course to expand platform adoption
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