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Data with 1+ years in SQL, Power BI, and Python automation
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Results-driven Data Analyst with around 2 years of experience at NielsenIQ, specialising in SQL-based data extraction, ETL quality assurance, Power BI dashboard development, and Python automation. Holds an M.Sc. in Statistics (Loyola College) with a strong foundation in descriptive statistics, regression analysis, and machine learning. Demonstrated ability to validate large-scale datasets, resolve data inconsistencies, and deliver actionable business intelligence to cross-functional stakeholders. Adept at translating complex data into clear KPIs, trend analyses, and performance metrics that drive data-driven decision-making across retail and consumer intelligence sectors.
Loyola College, Chennai
M.Sc. · Statistics
August 1, 2022 – June 30, 2024
Presidency College, Chennai
Bachelor of Science · Statistics
August 1, 2019 – June 30, 2022
NielsenIQ
Data Analyst
June 1, 2024 – April 1, 2026
Chennai, Tamil Nadu, India
Unified Customer Data Pipelines for 360° Experience Analysis
June 1, 2024 – June 1, 2026
• Designed and implemented an ETL pipeline to consolidate survey data, support tickets, and CRM records from 3 source systems into a centralised database for end-to-end customer experience tracking. • Performed data cleaning, normalisation, and validation to ensure dataset integrity; reduced duplicate records by 40% by standardising merge keys across data sources.
CX KPI Dashboards for Customer Satisfaction & Loyalty
June 1, 2024 – June 1, 2026
• Built interactive Power BI dashboards monitoring NPS, CSAT, churn rate, and ticket resolution time; enabled stakeholders to identify satisfaction trends in real time. • Applied rolling averages, KPI benchmarking, and variance analysis to surface actionable insights for customer retention strategies, improving dashboard adoption by 35%.
Clinical Decision-Making System (CDMS) — Multiple Sclerosis Detection
June 1, 2024 – June 1, 2026
• Developed a Logistic Regression predictive model to detect clinically confirmed Multiple Sclerosis (MS) cases with high precision. • Benchmarked model against Decision Tree and Random Forest using ROC-AUC, confusion matrix, and feature importance analysis; achieved best-in-class AUC score.
Comprehensive Retail Analytics & Multi-Model Prediction Platform
June 1, 2024 – June 1, 2026
• Built an end-to-end Python analytics solution encompassing EDA, feature engineering, and statistical hypothesis testing on large retail datasets. • Implemented regression, classification, and clustering models to generate insights for inventory optimisation, promotional planning, and churn reduction.
Python for Data Science
Coursera / NPTEL
June 1, 2026 – Present
Advanced SQL for Data Analytics
LinkedIn Learning
June 1, 2026 – Present
Microsoft Power BI Data Analyst Associate (PL-300)
In Progress (Expected: June 2026)
June 1, 2026 – Present
Google Data Analytics Professional Certificate
Coursera (Recommended)
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Statistics and professional experience as a Data Analyst at NielsenIQ align well with a data-driven culture. Their project diversity, including customer experience analysis, retail analytics, and clinical decision-making, shows adaptability and a broad interest in applying data skills across different domains. However, the limited professional experience (2 years) and the 'in progress' or 'recommended' status of several certifications suggest a candidate still developing their professional toolkit, which might require additional mentorship in a senior role.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through data inconsistency resolution and anomaly detection. Their collaboration with cross-functional teams indicates good teamwork and communication for operational fit. The ability to streamline workflows and automate tasks suggests an efficiency-oriented mindset.