• Developed an end-to-end ETL pipeline using AWS (S3, Lambda, Glue) to process education data, automating ingestion and transformation workflows into Redshift, and reducing manual effort by 60%

• Built a RAG-powered chatbot integrated with a vector database to enable natural language querying of internal datasets, streamlining access to insights and improving data reporting speed by 40%

1) Implemented a data ingestion pipeline using AWS Lambda, automating the extraction of customer engagement data from diverse sources into a centralized Amazon S3 data lake, reducing data retrieval time by 30%

2) Developed AWS Glue jobs to transform raw data and load curated datasets into S3 buckets, diminishing data preparation time by 40% and enabling ad-hoc querying via AWS Athena

3) Implemented AI-powered customer support chatbot for real-time query resolution, automating issue handling, and reducing manual support workload by 30%

1) Utilized Python and SQL to analyze data and collaborated with marketing teams to develop data-driven strategies, leading to $2.7M in incremental revenue within one quarter (source)

2) Developed and deployed a recommendation engine pipeline in AWS SageMaker, automating weekly data processing and enhancing marketing efficiency by 30%

3) Performed ad-hoc analysis to refine sampling activity strategy for stakeholders, resulting in improved resource allocation and a 10% reduction in operational costs

4) Built and maintained a Tableau dashboard integrated with Snowflake and AWS Athena, enabling real-time KPI tracking and enhancing operational effectiveness by 25% 

● Utilized Tableau and PowerBI to create visual reports for data and gain valuable insights from that data

● Analyzed past motor design data to accurately predict design parameters to reduce development cost by 7%

● Utilized Python libraries to process data generated from different sensors and employed unsupervised ML

approach (DBSCAN) to detect abnormalities in electric motors, resulting 98% fault detection accuracy

● Managed machine maintenance activities and implementing data analysis to forecast maintenance requirements, resulting in a 90% reduction in unforeseeable downtime

● Effectively managed a team of three individuals, demonstrating strong leadership and people skills, ensuring the timely completion of projects and tasks 

Analyzed solar irradiation data with the help of a Machine learning algorithm to predict the annual energy production of solar panels, optimal tilt angle, and estimate costs with up to 98% accuracy 

Conducted in-depth statistical analysis of electricity generation data, resulting in data-driven insights provided to the generation team, leading to a 15% improvement in accuracy for electricity production planning