About
Data Analyst with 3.5+ years of experience progressing from Junior Data Analyst to Data Analyst, specializing in SQL-based analytics, data quality, customer retention, financial risk analysis, and business reporting. Identified 8,101 additional delivered orders through order-status standardization, uncovered 239 hidden repeat buyers through customer identity resolution, and flagged seven high-risk accounts for manual review. Proficient in MySQL, Microsoft Azure database environments, Power BI, Excel, and Python, with experience in KPI definition, statistical hypothesis testing, data validation, and translating analytical findings into actionable business recommendations.
Skills & Expertise (33)
Work Experience
Data Analyst
Larc Software | Client: Capgemini
Jul 2024 - Present
Investigated a fall in the 90-day repeat purchase rate from 24.8% to 21.7% by analyzing 1 lakh+ orders across 11 tables in MySQL. Sized the drop at 451 lost repeat customers and ₹6.8 lakh in gross margin. Built a clean SQL layer without touching production data. Merged 1,167 duplicate customer emails, which uncovered 239 repeat buyers hidden across two accounts. Standardized 8,101 inconsistent order statuses and removed 5,175 fully refunded orders from the KPI. Tested leadership theories with cohort analysis. Showed that the rise in Instagram-acquired customers explained only 0.4 of the 3.1-point drop. Found late first deliveries to be a key driver: their share doubled from 12% to 26%, and those customers returned at 12–15%, against 25–26% when the first order arrived on time. Quantified a courier cost trade-off. Switching to a cheaper courier saved ₹1.3 lakh in shipping on East and North-East routes but cost ₹1.7 lakh in lost repeat-order margin, so the switch lost money overall.
Junior Data Analyst
Larc Software | Client: Capgemini
Apr 2023 - Jun 2024
Analyzed 800 financial transactions across 189 accounts and 180 customers covering January 2023–June 2024 to evaluate transaction behavior, account activity, and potential financial risk indicators. Identified data-quality issues, including non-unique transaction IDs, negative transaction amounts, and negative balances, distinguishing data anomalies from potentially meaningful financial signals. Developed Power BI dashboards to monitor account balances, transaction activity, overdrafts, and financial risk indicators, enabling stakeholders to identify accounts requiring further investigation. Developed account-level activity classifications and balance segments to assess transaction patterns and support structured customer financial analysis. Evaluated four risk indicators-overdrafts, large withdrawals, balance volatility, and high-risk scores- to identify accounts requiring further investigation. Flagged seven accounts triggering three of the four risk indicators and recommended manual review to support risk assessment and follow-up. Performed a one-sided t-test to evaluate whether accounts with higher transaction volumes maintained higher balances, obtaining a borderline statistically significant result (p ≈ 0.04). Documented analytical assumptions, data limitations, and recommended actions, including monitoring unusual balance fluctuations and reviewing overdraft cases.
Education
Bachelor of Technology (B.Tech), Computer Science and Engineering - DIT University
2017 - 2021 · Afghanistan
Certifications
Data Science Placement Course
Internshala · 2026
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Citizen
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Open to Relocation
Skills (33)
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