Vaisak Gopalakrishnan

• Reduced data preprocessing time by ~35% by engineering reusable Python ETL pipelines that standardized cleaning
and transformation across 4+ recurring datasets.
• Built 3 interactive Power BI dashboards tracking KPIs for internal stakeholders, cutting manual report generation from
2 days to under 2 hours per cycle.
• Authored 20+ optimized SQL queries against relational databases, enabling self-service analytics access for non
technical business units.
• Conducted EDA on customer and operational data surfacing 6 actionable trends adopted directly into quarterly
planning sessions.
• Co-developed and tested ML classification and regression models alongside senior analysts, improving baseline
predictive accuracy by 12% on pilot datasets.

• Analyzed and processed datasets using Python (Pandas, NumPy) and R, improving data accuracy and reducing
preprocessing time by 20%.
• Developed interactive dashboards in Power BI and Tableau for business intelligence reporting, improving KPI
tracking and boosting reporting efficiency by 30%.
• Engineered DAX measures and data models to deliver real-time business insights.
• Authored 10+ complex SQL queries to enhance data accessibility and support product and customer segmentation
decisions.
• Performed exploratory data analysis (EDA) to identify trends, patterns, and anomalies in large datasets.
• Built and evaluated machine learning models (Random Forest, Logistic Regression, Decision Tree, XGBoost) for
predictive analytics.
• Analyzed unstructured datasets using Python and SQL, reducing anomaly detection time by 25% and accelerating
insight generation and anomaly detection.

• Logged and categorized 100+ weekly gate-line incidents in the CRM system, producing structured incident reports
that reduced average fault resolution time by 20%.
• Cross-referenced ticketing data against passenger flow records during peak hours, identifying 3 recurring bottleneck
patterns escalated to operations management for action.
• Maintained 98%+ uptime of gate-line equipment by executing timed resets and documenting failure modes —
contributing to a measurable reduction in passenger-facing disruptions.

• Engineered a predictive model in Scikit-learn for a retail client that improved demand-forecast accuracy by 18%,
directly informing inventory replenishment strategy.
• Designed and deployed 5 Tableau & Power BI dashboards for finance and healthcare clients, enabling executives to
monitor real-time KPIs without analyst intervention.
• Reduced data pipeline processing time by 40% by restructuring legacy SQL queries and introducing indexed views
across a 2M+ row dataset.
• Architected structured data models from raw, unstructured datasets, slashing machine learning model training time
by 25% and elevating model precision scores by 6%; presented findings to cross-functional teams of 8 members.
• Delivered end-to-end analytics across finance, retail, and healthcare verticals — translating stakeholder requirements
into actionable analytical frameworks.

• Delivered 10+ responsive web applications in React.js and JavaScript (ES6+), reducing average page load time by 30%
through component optimization and lazy loading.
• Maintained 100% cross-browser compatibility across Chrome, Firefox, and Safari for all production releases over a 5-
year period.
• Contributed to CI/CD pipeline configuration via Git/GitHub, reducing deployment cycle time from weekly to bi-daily
releases within an Agile/Scrum team of 8.