M Lakshmi Keerthana

Data Analyst

Bengaluru, India 0+ yrs exp 85 · Excellent

About

Data Analyst who turns messy, real-world data into reliable insight using SQL, Python, and statistical reasoning. Skilled in building dashboards and reports that drive business decisions, cleaning and validating datasets, and communicating findings clearly to technical and non-technical stakeholders. Comfortable taking analysis from prototype toward production, partnering cross-functionally on metrics and success criteria, and championing data quality, reproducibility, and ethical, privacy-conscious use of data and models.

Skills & Expertise (39)

Python Intermediate

7.5/10

Years Exp

Pandas Intermediate

7.5/10

Years Exp

NumPy Intermediate

7.5/10

Years Exp

SQL Intermediate

7.5/10

Years Exp

Statistical Reasoning Intermediate

7.0/10

Years Exp

Data Validation Intermediate

6.5/10

Years Exp

Power BI Intermediate

6.5/10

Years Exp

Tableau Intermediate

6.5/10

Years Exp

Feature Engineering Intermediate

6.0/10

Years Exp

Data Governance Intermediate

5.5/10

Years Exp

ETL Intermediate

5.5/10

Years Exp

Precision Git GitHub DBT Model Evaluation Accuracy Charts Recall F1-Score Recommendation Systems Classification Financial Services Compliance SDLC Data Pipelines Dashboard Development Power Pivot Power Query Pivot Tables Excel EDA Hypothesis-Driven Analysis scikit-learn Performance Tuning Window Functions Aggregations Joins MySql

Work Experience

Data Science Intern (Virtual)

Arka Mediaworks

Nov 2025 - Jan 2026

Partnered across functions to turn unstructured, messy datasets into reliable insight, running EDA to surface user interaction patterns and behavioral trends that informed business decisions. Cleaned, transformed, and engineered features from raw data using Python (Pandas, NumPy), improving data quality, validity, and reproducibility ahead of downstream modeling and reporting. Took a content-based recommendation model from prototype to a working system using TF-IDF vectorization and cosine similarity, applying statistical reasoning to evaluate and improve match quality. Communicated analysis and trade-offs clearly to both technical and non-technical stakeholders through structured reports and visualizations, supporting decisions from discovery through performance monitoring.