Senior Data Analyst at Apple — Great IT Group

Collaborated with engineering teams to analyze build and deployment logs, reducing CI/CD failure rates by 30%. Established dashboards using Power BI and Kusto to monitor release velocity, exception spikes, and rollback trends. Proposed performance KPIs to leadership, improving release readiness across 3 codebases. Systematized test coverage metrics by integrating Azure DevOps queries and GitHub events. Translated software lifecycle bottlenecks into actionable insights using statistical trend analysis and data mining. Presented experiment outcomes and A/B test results to product managers to inform release gating decisions. Partnered with stakeholders to define telemetry requirements and reduce gaps by 40%. Mechanized data quality checks for engineering logs. Built forecasting models to anticipate deployment delays, increasing release predictability by 23%. Mentored on Git workflows and data storytelling. Developed reproducible pipelines in Python and SQL for real-time build health alert

Senior Data Analyst at Concentrix

Developed SQL and Python pipelines that reduced QA processing time by 48%. Planned statistical tests to refine ML model thresholds, improving eligibility prediction stability by 22%. Computerized outlier detection systems, identifying 96% of issues before production deployment. Partnered with SCOT engineering to align model assumptions with production data constraints. Generated QuickSight and Tableau dashboards to track exception patterns, cohort trends, and policy adherence. Summarized findings for executive stakeholders across 5+ divisions to support operational alignment. Delivered reproducible documentation for QA/UAT cycles. Mentored analysts on experimental design and data engineering collaboration. Standardized root cause metrics and implemented automated drift monitoring. Achieved 99% SLA compliance through enhanced batch monitoring. Introduced variance-based thresholds that cut false positives by 31%. Supported audit reviews and monthly benefit compliance reports.

Data Analyst at Wipro Technologies

Engineered ETL pipelines for predictive models guiding $5M+ in social benefit disbursements. Conducted logistic regression to surface eligibility biases, reducing misclassifications by 17%. Developed A/B testing plans for benefit approval models across three workflows. Established Power BI and R Shiny dashboards to monitor model accuracy and drift. Initiated synthetic dataset generation to enhance QA readiness and scenario-based testing. Proposed dashboard redesigns to improve stakeholder understanding of outputs. Reduced manual QA by 40% through rule-based test scenario automation.