• Developed and deployed predictive models on large scale HR data to forecast attrition, retention, optimize recruitment and improve workforce planning through scalable ML solutions and interactive dashboards.
• Built predictive models (logistic regression, random forests, XGBoost) to forecast attrition, identify high performers and estimate recruiting demand, directly supporting strategic workforce planning.
• Led end-to-end analytics and ML initiatives to optimize the hiring pipeline, reducing time-to-fill and improving candidate experience through data-driven insights and measurable business impact.
• Queried, cleaned, and transformed large-scale HR datasets using SQL and Apache Spark, accelerating feature engineering, model development, and analytical workflows.
• Designed and executed A/B tests and statistical experiments to evaluate hiring strategies, improving recruiter effectiveness and conversion rates.
• Deployed machine learning models using FastAPI, Docker, and MLflow on AWS (EC2, Lambda, S3), while collaborating with BI and data engineering teams to strengthen data pipelines, automation, and data quality, supported by Git-based version control.
• Developed interactive dashboards using Tableau and Power BI to track key HR metrics like turnover, hiring efficiency and productivity which helped non-technical stakeholders in making data-driven decisions.
• Trained junior analysts and guided them through data preprocessing, choosing the right models, optimization and building dashboards.