Shashi Rajak

Worked for a Banking and Credit card service provider company

• Led a project focused on customer segmentation and churn prediction, utilising advanced machine learning techniques
• Developed models to identify customer segments and predict churn behaviour, resulting in a 10% reduction in churn rates
• Implemented data preprocessing pipelines and feature engineering techniques to improve model performance.
• Collaborated with cross-functional teams to integrate predictive models into the bank's decision-making processes
• Created an interactive Dashboard for storytelling and benchmarking to present data driven insights to stakeholder

Worked for An Insurance Broker firm

• Worked on Data orchestration
• Worked on Medallion DB architecture(bronze,silver,gold)
• Worked on Azure Data Factory(ADF), Azure Synapse and Created ETL Pipelines.
• Created Json to read and standardize data through sql script
• Automated creation of json and table creation script for more than 300 data objects

Worked for a company focused on cloud migration.

• Responsible for data gathering.
• Performed exploratory data analysis.
• Transformed the data into a graphical structure.
• Identified the Logical Application Group, Communities, and Shared Services by creating clusters from discovery data.
• Streamlined the cloud migration process and reduced overall time by 60%.
• Created a Flask API to consume real-time data and send a response to the front end.
• Team leading and stakeholder management.

Company specialized in data management.

• Prepared the dataset.
• Implemented NLP techniques and machine learning models to automate document processing tasks, reducing manual effort by 20%.
• Collaborated with the engineering team to integrate the document classification system into the company's document management platform.

Leading global technology company (a Swedish MNC).

• Led data cleaning and transformation efforts for client-provided data to meet IBM Tririga format requirements.
• Developed scripts and processes to automate data cleansing and transformation tasks.
• Ensured data integrity and consistency, resulting in a successful migration and system integration.
• Automated data migration processes improve efficiency and reduce manual effort.
• Developed scripts and workflows to extract, transform, and load data into the target system.
• Implemented validation checks to ensure data accuracy and consistency.
• Automated manual tasks using Python and pandas, reducing processing time and errors.