Nivas Kolla

Data Analytics & Engineering Analyst - Infosys

(2025-10 - 2026-02)

Captured and formalised analytical requirements from BRDs and product managers, translating business questions into structured SQL- and Python-based pipelines that delivered clean, consumable datasets to stakeholders.
Built and maintained Power BI intelligence dashboards and KPI reporting frameworks, surfacing trends, anomalies, and performance metrics to enable data-driven decisions for both technical and non-technical audiences.
Performed rigorous data profiling and EDA across large structured datasets, identifying data quality issues and collaborating with the DE team to resolve upstream gaps — increasing reporting accuracy and reducing downstream errors.
Coordinated with the Data Science team to conduct analytical QA on model outputs; interpreted statistical and predictive model results and translated findings into actionable business insights presented to senior stakeholders.
Executed data validation and reconciliation against client control totals, ensuring completeness and accuracy of analytical outputs across multiple reporting layers.
Mentored and guided junior analysts on SQL best practices, dashboard design standards, and data governance protocols — growing team capability and analytical output quality.
Documented transformation logic, data lineage, and validation procedures to meet audit, compliance, and data governance requirements; acted independently to deliver projects to schedule and scope.

Data Science & Analytics Intern - YBI Foundation

(2024-06)

Designed SQL and Python pipelines to integrate structured datasets from multiple sources, supporting consistent data quality for downstream analysis and dashboard reporting.
Conducted EDA to surface patterns, correlations, and anomalies across large datasets; built Power BI dashboards to communicate KPIs and analytical findings to both technical and business stakeholders.
Supported deployment of supervised predictive models, applying feature engineering and evaluation techniques; summarised model outputs into insight narratives for business teams.
Automated data ingestion workflows, reducing manual processing overhead and improving pipeline reliability for recurring analytics tasks.

Machine Learning Engineering Intern - Teachnook

(2023-01 - 2023-03)

Built and deployed supervised learning models, achieving 15% improvement in prediction accuracy through feature engineering, hyperparameter tuning, and rigorous model evaluation.
Interpreted post-deployment model performance metrics and visualised outcomes via dashboards, communicating model behaviour and business impact to stakeholders in clear, non-technical language.
Developed end-to-end data preprocessing pipelines ensuring data integrity across multi-source datasets prior to model training and production deployment.

Marketing Data Analyst Intern - Shine Projects

(2024-05 - 2024-06)

Engineered SQL and Excel-based analytical pipelines to profile customer behaviour and campaign performance data, delivering insights that directly improved marketing targeting strategies.
Built structured KPI dashboards and implemented validation procedures to ensure data accuracy and consistency across marketing reporting — aligning outputs with business strategy goals.
Collaborated with marketing and operations stakeholders to translate business requirements into pipeline outputs and analytical findings, presenting results clearly to drive action.

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