Mayank Sharma

Built solution to predict probability of credit card application approval based on customer's income, Age, occupation type etc. Conducted EDA, data cleaning, and feature engineering using Scikit-learn, matplotlib, Numpy, and Pandas. Utilized logistic regression, decision tree, random forest classifier, XGBoost and CATBoost models for prediction. Achieved precision score of 98% using hyperparameter tuning.

Drafted proposal email to acquire client and suggested required changes in data gathering process to get valuable insights further. Created powerpoint presentation to make client understand about life cycle of whole project. Developed comprehensive Power BI Dashboard for prominent KPMG client, enabling them to forecast potential target customers and subsequently elevate their customer preference strategies.

Used SQL and Python to enhance recruitment strategies. Analyzed candidate-client data with SQL, improving sourcing efficiency. Employed Python for accurate data refinement and automation. Created actionable reports through SQL and Python. Collaborated with recruitment team for tailored solutions.

Developed and maintained advanced Excel-based tools to streamline recruitment processes, resulting in 20% reduction in processing time. Worked closely with recruitment team to gather requirements for customized reports and interactive dashboards. Implemented data-driven strategies for candidate sourcing, leveraging Excel data analysis capabilities to identify high-potential channels. Created interactive dashboards using pivot tables.

5+ Years experience as US IT Recruiter.