Experienced in data analysis, requirements gathering, and project management. Adept at utilizing analytical skills to identify and address business needs. Strong understanding of teamwork and adaptability, delivering impactful solutions that drive success.
Built a machine learning-based predictive maintenance model using Random Forest, LSTM, SVC and Decision Tree Classifier, achieving 98.2% failure prediction accuracy. Engineered 15+ time-series features from IoT sensor data, reducing unplanned downtime by 30% and maintenance costs by 25%. Integrated real-time dashboards (Power BI) for proactive alerts and decision-making.
Industry 4.0 & 5.0 Tech Impact AnalysisAnalyzed the role of IoT, AI, and robotics in manufacturing efficiency using logistic regression and correlation matrices. Demonstrated a 15% increase in productivity and developed an IoT-based predictive maintenance model that led to a 20% cost reduction. Validated findings through statistical hypothesis testing and OEE metrics.
Alternative Fuel Engine Market ResearchConducted market research and sentiment analysis on alternative fuel technologies, collecting insights from 200+ stakeholders (taxi drivers, pedestrians). Built an NLP-based sentiment model to identify adoption trends, improving alignment of fuel tech strategy with consumer preferences by 25%.