Data Analyst with less than a year in Applied Statistics & Data Analysis
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Passionate Data Analyst with an M.Sc. in Applied Statistics and strong analytical and problem-solving skills. Experienced in collecting, analyzing, and interpreting complex datasets to identify trends, patterns, and growth opportunities that support data-driven decision-making. Skilled in creating reports and dashboards using Tableau and Power BI, and proficient in Excel, SQL, and Python for data analysis and reporting. Delivered three end-to-end data analytics projects in insurance and capital markets with measurable business impact. Demonstrates excellent communication and presentation skills, attention to detail, and a collaborative approach to improving business performance.
Sri Venkateswara University
M.Sc. · Applied Statistics
August 1, 2022 – June 30, 2024
Gayatri Vidya Parishad College
B.Sc. · Mathematics, Statistics & Computer Science
August 1, 2019 – June 30, 2022
NSE Sector Performance Intelligence Dashboard
March 1, 2026 – April 1, 2026
Collected and analyzed 23,501 rows of live NSE stock data across 48 companies and 10 sectors — building an automated data pipeline from ingestion to reporting. Interpreted complex datasets to identify return trends, volatility patterns, and sector-level growth opportunities across a 2-year period. Created an interactive Power BI dashboard for business insights — dark-themed KPI summary cards, year and sector slicers, and cross-sector performance benchmarking visuals. Supported data-driven decision-making with a clear sector allocation recommendation — FMCG for stability, Realty/Infra/Energy for growth. Ensured data accuracy and quality — documented all data analysis processes and methodologies in a structured GitHub README for team collaboration.
View ProjectA/B Testing - Insurance Pricing Strategy Analysis
February 1, 2026 – March 1, 2026
Collected and analyzed data across 5,000 policyholders to evaluate two pricing strategies — applying rigorous statistical analysis to support data-driven decision-making. Identified patterns in customer lapse behaviour and growth opportunities — demonstrated risk-based pricing reduces lapse rate by 3.9pp (p = 0.0019) and generates ₹1.93M revenue uplift. Created interactive Power BI reports and dashboards for business insights — KPI cards, slicers, and segmentation visuals enabling stakeholder decision-making.
View ProjectMotor Insurance Claims Risk Analysis
January 1, 2026 – February 1, 2026
Collected, analyzed, and interpreted 10,301 real insurance records to identify trends and patterns in policyholder claim behaviour — supporting data-driven decision-making at the underwriting stage. Performed end-to-end data analysis including data cleaning, exploratory analysis, and predictive modelling — built a logistic regression classifier achieving ROC-AUC of 0.81 with 76% recall. Identified key growth opportunities and risk patterns — urban drivers with revoked licences claim 3x more than rural; Panel Trucks generate highest average claim amounts. Created an interactive Tableau dashboard for business insights — enabling self-service data-driven recommendations for non-technical stakeholders. Ensured data accuracy and quality throughout — performed quality assurance, validated results, and documented full methodology.
View ProjectACET - Cleared
Institute of Actuaries of India (IAI)
June 1, 2026 – Present
CB1 Business Finance - Cleared
Institute of Actuaries of India (IAI)
June 1, 2026 – Present
CB2 Business Economics
Institute of Actuaries of India (IAI)
June 1, 2026 – Present
CS1 Actuarial Statistics - Cleared
Institute of Actuaries of India (IAI)
June 1, 2026 – Present
Analyzing Data with Python
IBM via edX
January 1, 2024 – Present
Data Analytics & Visualisation Job Simulation
Accenture North America via Forage
January 1, 2024 – Present
Data Analytics Program (Excel, SQL, Python, R, Tableau)
CedLearn, Hyderabad
January 1, 2023 – Present
Power BI Training
Jobaaj Learnings & Office Master
January 1, 2023 – Present
The candidate scored 58% on the Python Internship Test, indicating a basic to intermediate understanding of the core concepts but with significant room for improvement in advanced topics and practical application.
Strengths
Limitations
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive approach to applying theoretical knowledge to real-world scenarios, particularly in the insurance and financial sectors. The diversity of projects (risk analysis, sector performance, A/B testing) shows a broad interest in different data analysis applications. The certifications and actuarial qualifications indicate a strong commitment to continuous learning and professional development. The emphasis on collaboration and clear communication in project descriptions aligns well with a team-oriented environment. The candidate's target role of Data Analyst is a strong match for their skills and project experience.
Soft Skills & Operational Fit
The candidate's resume highlights strong communication and presentation skills, attention to detail, problem-solving aptitude, and a collaborative approach. These traits are essential for a Data Analyst who needs to interpret complex data and communicate findings to both technical and non-technical stakeholders. The project descriptions emphasize documentation and quality assurance, indicating a structured and reliable work style. The psychometric test score of 300/500 suggests average logical reasoning and work attitude, with potential areas for development in stress handling or team collaboration, which would require further probing.