Kavita Bijarniya

Results-driven Data Analyst with 1+ year of experience leveraging SQL, Power BI, and Excel to uncover trends, monitor KPIs, and drive informed business decisions. Experienced in building interactive dashboards and delivering end-to-end analytics solutions from data extraction to stakeholder presentation.

Work experience

▲ Unified Mentor Private Limited ( Sep 2025 - Dec 2025 )

Data Analyst Intern

Developed 3 end-to-end Power BI dashboards tracking 15+ KPIs across sales, supplier, and customer performance — reducing manual reporting effort by 30% and improving insight delivery speed by 40%.
Performed customer segmentation on 50K+ transactions using SQL (joins, CTEs, window functions), uncovering that 30% of customers drove 65% of revenue — directly informing retention strategy.
Built advanced DAX measures for YoY comparisons, trend analysis, and performance indicators, enabling data-driven decisions across 4–5 cross-functional stakeholder teams.
Cleaned and transformed 100K+ rows via Power Query, achieving >98% data accuracy and cutting report preparation time by 30%.

▲ Evoastra Ventures PVT LTD ( Jan 2024 - Jun 2024 )

Data Science Apprenticeship

Cleaned and analysed datasets of 10K+ rows in Excel, resolving missing values, duplicates, and formatting issues — developing production-ready data quality practices transferable to SQL and BI tools.
Built summary reports using advanced formulas (VLOOKUP, INDEX-MATCH, SUMIFS) and Pivot Tables, delivering structured insights to support business operations.
Established a strong foundation in exploratory data analysis (EDA) and data handling, accelerating readiness for enterprise SQL and Power BI environments.

▲ Technocolabs Softwares Inc. ( May 2023 - Jun 2023 )

Machine Learning Engineer

Built a sales forecasting model for BigMart using regression techniques in Python, predicting outlet-level inventory demand and directly supporting supply chain planning decisions.
Developed a credit risk classification model for P2P lending, achieving 84% accuracy in predicting loan defaults — enabling data-driven risk prioritisation for lenders.
Executed full ML pipeline across both projects — data preprocessing, feature engineering, model training, and results visualisation using Pandas and Matplotlib.
Applied predictive analytics to real business problems in retail and fintech, building practical experience in Python-based machine learning for decision support.