o Collaborated with Engineering, Failure Analysis, and Customer Support teams to identify the root causes of increased Defective Parts Per Million (DPPM) through comprehensive analyses at both assembly and component levels. Implemented prevention measures to reduce DPPM, and forecasted future trends while monitoring DPPM improvement on a weekly basiso Conducted ACV failure, grid level failure and low DCV failure analyses on microinverters to identify product shortcomings and improvement opportunities; analyzed geographical data in python to uncover grid-level issues.o Automated data updating and management tasks in Excel using Python ETL processes, reducing processing time from hours to 5 minutes.o Collaborated with cross-functional teams to extract and analyze data from Redshift and S3 databases using Spark SQL, PySpark, Python, R, and Incorta, producing tailored reports that help in informed decision-making. Automated report generation into interactive dashboards with visualizations, providing actionable insights on product performance.o Developed and maintained complex, fully automated dashboards in Incorta, meeting stakeholder requirements for effective metric monitoring; worked with IT teams to enhance data response times and accuracy.

Roles & Responsibilities: o Collaborate with manufacturing clients to understand and address challenges and failures in various manufacturing processes, leveraging Artificial Intelligence (AI) to develop effective solutions.o Proficient in working with manufacturing machines data formats, time series data format, including data cleaning and defining performance measures based on data pre-processing. Projects:o Developed a data monitoring & data check pipeline which downloads the data from the AWS S3 bucket using boto3 library; the data monitoring pipeline calculates the traceability and scrap rate, and the data check pipeline prepares the reports in JSON format for anomalies and outliers in the data, which helped clients to track the quality of product produced in the daily process.o Worked with the Product team to develop and generalise the pipelines of Sensor alert data, which downloads the data from the AWS time stream, and does the data pre-processing, EDA. And pushes the result on the RDS table; in case of alerts, it shows on the Grafana dashboard. o Collaborated with the software team to maintain and update (CI/CD) pipelines on Amazon Web Services (AWS), ensuring smooth and efficient deployment of ML pipelines on AWS lambda.o Performed various data analyses using Plotly to identify the impact of sensor data, such as Pressurization, air and water cooling channels, and its effect on product quality, i.e. label.o Utilized domain knowledge to connect the machine data of melting, casting & furnace; identified its correlation with product quality; Performed K means clustering to find the least scrap rate cluster for different Parameters, prescribing those ranges resulting 25% reduction in scrap rate.o Designed and developed data Extracting Transforming pipeline using Python for a project, resulting in a 30% reduction in data processing time and improved data accuracy.

o Data Analysis and Interpretation | Solid Mechanics | Aerospace Structural Mechanicso Assisted in grading the coding assignments, clearing student’s doubts in programming projects and Coordinated & facilitated online tests & quizzes to ensure a smooth online exam process.