Siddhi Gunaji

• Built scalable forecasting pipelines using Nixtla models and Python, automated anomaly detection, and data reporting using GitLab CI/CD and BigQuery to support business-critical decision-making. • Conducted exploratory data analysis using Python and SQL to identify forecastability issues, detect anomalies, and speed up reporting workflows, significantly reducing manual validation and improving client communication on data quality.

• Addressed unplanned downtime in industrial setups by developing a predictive maintenance model using LSTM networks on pneumatic cylinder data extracted from PLC signals, resulting in a 20% improvement in failure prediction accuracy. • Performed in-depth time series data analysis and model optimization, enabling proactive scheduling and reducing maintenance costs through early anomaly detection.

• Extracted meaningful insights from over 4.6 million records of PLC signal data using advanced big data methods like NumPy arrays, Dask, and Polars, enhancing data understanding and predictive maintenance capabilities. • Performed exploratory data analysis (EDA) and applied multiple machine learning models on cutting machine tool data in the engine stator production station, achieving approximately 72% accuracy in predicting blade replacement timings and improving production efficiency.

• Engineered and optimized tax fraud detection systems using Apache Kafka, Spark, and Airflow for real-time data ingestion and ETL, and deployed anomaly detection models with TensorFlow and AWS SageMaker, enhancing fraud detection and system efficiency. • Developed and maintained SQL queries and stored procedures using Teradata at the Central Board of Direct Taxes, leveraging Big Data expertise for effective data management and analysis.