• A competent professional with nearly 8 years of experience in Data Quality Assessment entailing Data Analysis, Data Validation, Profiling, and Cleansing. • Presently working with EPFO organization Varanasi(Ministry of labour gov of India) as a IT Technician . • Adept at carrying out continued improvement of data quality through investigation and rectifying quality issues. • Exposure in end-to-end development of projects from inception, requirement specs, planning, designing, implementation, documentation and closure. • Capabilities in understanding the needs of the project, its strategic direction and identifying initiatives that will allow a project to meet those strategic goals; coordination of data hand off for analysis and integration into reporting. • Well versed with statistical techniques such as Regression Analysis, Logistic Regression and Time Series/Forecasting. • Drawing relevant conclusions from the reports developed/ submitted and providing the same to the top management in taking critical decisions. • Undertaking analysis and assessment of large volumes of customer data using data mining tools to determine customer behavior & trends. • Compiling large amount of primary and secondary data from different sources. • Collecting data/facts, analyzing these to find root cause of business problem and then put forward key findings to the management. • Devising optimum ways and methods to improve the data hygiene and quality by cleaning up of junk datas, standardizing & validating data, floating out data into relevant fields. • Utilizing data profiling and data quality tools, as well as with various data sources to uncover and determine root causes of data quality issues. • Collaborating with the business data owners to establish the quality business rules that will provide the foundation of the organisation's data quality improvement plan. • Establishing a data quality methodology documenting a repeatable set of processes for determining, investigating and resolving data quality issues, establishing an on-going process for maintaining quality data, and defining data quality audit procedures • Handling over 20 projects for profiling and testing of client journal entry population as per 199 guidelines - Statement on Auditing Standards No.99 • Testing journal entry by reconciling to trial balance and performing a series of data quality checks. • Determining data analysis procedures were performed on the journal entry data to identify journal entries exhibiting certain characteristics of audit interest. • Mentoring a team of 3 to 4 consultants; understanding the analysis scope and requirements; reviewing deliverable, tracking project status and hours spent, and interfacing with US based teams. • Taking part in study to enhance the process of selection of potential fraudulent entries identified by audit teams; improving the methodology of calculating the risk scores used for flagging fraudulent journal entries. • Training & guiding new joiners