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VamshiKrishna Challa
Mid-level Data Scientist specializing in industrial IoT, predictive analytics, and generative AI
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About
ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).
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Key Strengths
- Built Industrial IoT anomaly detection combining LSTM/GRU with PCA and Isolation Forest on multivariate sensor time series
- Designed AWS-based ETL/ML pipelines (S3 + PySpark) and surfaced results in Tableau/Power BI dashboards
- Linked anomalies to similar past events and maintenance notes using transformer embeddings + vector search; reduced manual review time ~35%
- Improved data quality via entity resolution and reconciliation (Python/Pandas/SQL); achieved 35% drop in preprocessing errors and improved model accuracy
- Production-grade workflow practices: modular pipelines, orchestration (Airflow/AWS workflows), structured logging, retries/alerts, quarantine tables
- Strong model validation and stakeholder alignment: monitored precision/recall and drift; moved to production after demonstrating 15% lift at agreed false-positive rate with monitoring/rollback plan
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