Devanshi Joshi

Junior Analyst

Bengaluru, Karnataka, India 0+ yrs exp 85 · Excellent

About

Final-year B.Tech Data Science graduate (CGPA: 8.62) with hands-on experience collecting, organizing, cleaning, and validating business data from multiple sources to support reporting and decision-making. Advanced Excel user comfortable with pivot tables, VLOOKUP/XLOOKUP, INDEX-MATCH, SUMIFS/COUNTIFS, and conditional formatting; skilled in SQL for querying relational databases and in Power BI/Tableau for building dashboards that track KPIs and business performance. Detail-oriented, reliable under deadlines, and experienced translating analytical findings into clear summaries for cross-functional stakeholders in a remote environment.

Skills & Expertise (26)

Pivot Tables Advanced

8.0/10

Years Exp

Excel Advanced

8.0/10

Years Exp

VLOOKUP Advanced

7.5/10

Years Exp

XLOOKUP Advanced

7.5/10

Years Exp

Index-match Advanced

7.5/10

Years Exp

Conditional Formatting Advanced

7.5/10

Years Exp

Data Validation Advanced

7.5/10

Years Exp

SUMIFS Advanced

7.5/10

Years Exp

Countifs Advanced

7.5/10

Years Exp

Data Filtering Advanced

7.5/10

Years Exp

Python Intermediate

7.0/10

Years Exp

Data Cleaning Intermediate

7.0/10

Years Exp

Joins Intermediate

7.0/10

Years Exp

SQL Intermediate

7.0/10

Years Exp

Subqueries Intermediate

6.5/10

Years Exp

Aggregations Intermediate

6.5/10

Years Exp

Window Functions Intermediate

6.5/10

Years Exp

MySql Intermediate

6.5/10

Years Exp

Postgresql Intermediate

6.5/10

Years Exp

Power BI Intermediate

6.0/10

Years Exp

Tableau Intermediate

6.0/10

Years Exp

Dashboard Design Intermediate

6.0/10

Years Exp

KPI Tracking Intermediate

6.0/10

Years Exp

Deduplication Intermediate

6.0/10

Years Exp

Documentation Intermediate

6.0/10

Years Exp

Cross-Functional Communication Intermediate

6.0/10

Years Exp

Work Experience

Data & AI Analyst Intern

Quest Digiflex

Apr 2025 - May 2025

Improved data accuracy by 28%, measured against source records, by collecting, cleaning, and validating data from multiple structured sources using Excel and Python before downstream reporting. Cut dataset turnaround time by 40%, measured across recurring data pulls, by building structured Excel-based validation frameworks and automated extraction scripts with systematic quality checks. Eliminated recurring data-entry errors, measured through pre- and post-implementation audits, by designing standardized templates and validation checklists adopted for ongoing reporting cycles. Flagged trends and inconsistencies in operational data, measured via EDA outputs, by analyzing datasets in Python and Excel and summarizing findings for stakeholders in plain language.