Dinesh Kumar Chalamalasetty

Performed advanced SQL-based data analysis, including joins, aggregations, and complex queries for data validation and reconciliation.

Conducted data profiling, cleansing, and quality checks across large insurance datasets, ensuring accuracy and completeness.

Investigated data discrepancies and performed root cause analysis to resolve data quality issues.

• I was involved in two main tasks which are data cleaning, and exploratory data analysis (EDA), and allowed to access running build models, being a fresher not allowed to work on it but allowed to access it. I always need to make check on data integrity which involves validity, Accuracy, consistency, etc if the data is irrelevant or any missing values in data then it leads to incorrect model building which shows the wrong calculated score for risk metrics as the project is about to provide the dashboard with risk scenarios and risk percentages.
• Environment: Python, AI, Machine Learning, Data Cleansing, MySQL, React with Material UI
• Team Size: 23
• Responsibilities:
• Data cleansing.
• Work closely with our Data Scientists and gain valuable AI/ML skills.
• Lead the entire life-cycle of a cutting-edge AI/ML project.
• Assist your team in day-to-day jobs and projects.
• Create forecasting models by applying Machine Learning algorithms.
• Research and development of new optimization techniques.
• Optimise the applications and discover the most efficient ways of working.
• Communicate progress on projects throughout the summer.
• Present findings to the team at the end of the program.