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Data Analyst with less than a year in SQL, Python, Power BI, and Advanced Excel.
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B. Com (Hons) student at Delhi University with hands-on command of SQL, Python, Advanced Excel, and Power BI — covering the full analytics workflow from data cleaning and wrangling through modelling, exploratory analysis, and dashboard storytelling. Has delivered end-to-end analytics projects spanning e-commerce performance, customer intelligence (RFM, cohort, churn, CLV), and multi-region paid-media analytics, with proficiency in DAX, star-schema design, Power Query ETL, and KPI reporting. Mathematics-grounded foundation paired with strong attention to data accuracy and stakeholder-ready communication.
Delhi University
Bachelor of Commerce (Honours) · Commerce
January 1, 2023 – January 1, 2026
Deloitte Australia - Data Analytics Job Simulation
January 1, 2026 – January 1, 2026
Analysed a client's operational dataset to classify data and flag units operating outside acceptable thresholds, supporting a forensic-style investigation into anomalous activity. Interrogated the data in Tableau to surface trends and outliers, translating raw figures into clear, decision-ready insights for stakeholders.
J.P. Morgan - Quantitative Research Job Simulation
January 1, 2026 – January 1, 2026
Analysed historical commodity price data in Python to model seasonality and extrapolate future prices, then built a pricing function for a commodity storage contract. Developed a credit-risk model estimating borrower probability of default and expected loss, and applied FICO-score quantisation to sharpen risk segmentation.
E-Commerce Executive Analytics
January 1, 2026 – January 1, 2026
Analysed an end-to-end Indian e-commerce business — 598M GMV, 18K orders, 5,000 customers — to surface executive insights across sales, customers, marketing, and operations. Cleaned and standardised raw transactional data — handled nulls, deduplicated customer records, normalised category and date hierarchies, and validated cross-table referential integrity across 6 source tables to produce an analysis-ready dataset. Engineered 10+ analytical metrics (Net Revenue, Gross Margin %, AOV, ROAS, CLV, RFM Score, YoY Growth) across orders, customers, products, returns, and marketing dimensions. Analysed sales performance — Net Revenue ₹567M, AOV 32K, Gross Margin 30.2%, Return Rate 10.5% — tracking monthly revenue growth (₹1M → ₹45M, Jan-24 to Nov-25) and revenue concentration across Indian states. Segmented 5,000 customers through RFM and cohort analysis (Regular 46%, New 30%, VIP 15%, At-Risk 9%), isolating high-value cohorts averaging ₹119.5K CLV. Analysed marketing efficiency across 7 channels (₹43.4M spend, ROAS 51x) using CAC-vs-CLV comparison and a conversion funnel (Sessions 11M → Add-to-Cart 8% → Checkout 5% → Purchase 3.1%) to locate drop-off points. Identified ₹4M/month return-value leakage and root-caused it through Pareto 80/20 brand analysis and delivery-SLA breakdown by city tier.
Quantium - Data Analytics Job Simulation
January 1, 2026 – January 1, 2026
Cleaned and validated retail transaction and customer data in Python (Pandas), engineering customer-segment features to uncover the key drivers of purchasing behaviour within a product category. Designed a control-vs-trial store experiment, applying statistical significance testing to measure sales uplift and distilling the results into a clear commercial recommendation.
Customer Retention & Churn Analysis
January 1, 2026 – January 1, 2026
Analysed 5,000+ customers to quantify churn drivers and long-term retention patterns across acquisition channels and customer segments. Quantified an overall churn rate of 20.4%, segmenting the base into Active (4K) and At-Risk (1K) with an average CLV of $155.3K and mean tenure of 423 days. Identified acquisition channel as the strongest churn driver – Direct churned highest at 31.2% versus Referrals at just 5.1% — and found new customers churned at 22.8% against only 8.7% for VIPs. Conducted cohort retention analysis across 12 monthly cohorts (Jan-Dec 2024), measuring average month-1 retention of 21.8% and an average customer lifetime of 8.4 months, with the Jan 2024 cohort leading on both retention (78.4%) and revenue ($220K). Concluded that new customers churn fastest, pinpointing early onboarding and loyalty programs as the highest-impact retention levers.
Meta Ads Lead-Generation Analysis – Multi-Region (APAC)
January 1, 2026 – January 1, 2026
Analysed multi-country Meta Ads lead-generation campaigns across Malaysia, Singapore, Thailand, and Malaysia (Startup) to enable region-level ROI comparison and data-driven budget reallocation. Standardised cross-market campaign data — normalised currency, unified campaign-naming conventions, and validated lead-attribution flags — to enable like-for-like efficiency comparison across 4 APAC markets. Analysed paid-media metrics (Spend, Leads, CPL, CTR, CPC) to compare campaign efficiency across markets and isolate the strongest and weakest performers. Identified a 2.1× CPL efficiency gap between Singapore (₹727 CPL, 2.15% CTR) and Malaysia Startup (₹1,509 CPL, 2.61% CTR) through cross-market analysis. Recommended budget reallocation, flagging Singapore as the highest-ROI market and Malaysia (₹1L+ spend) as the priority for efficiency optimisation.
Computer Operations & Advanced Excel
Sardar Patel Institute of Research & Technology, Delhi
June 1, 2026 – Present
SQL Certification
Study Trigger Coaching, Delhi
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project work, including job simulations from Deloitte, J.P. Morgan, and Quantium, demonstrates an interest in diverse industry applications (e-commerce, finance, retail, marketing). This breadth of exposure, combined with a focus on practical problem-solving, suggests a good cultural fit for dynamic, data-centric environments. The candidate is currently pursuing a Bachelor's degree, indicating a strong academic foundation and a commitment to continuous learning.
Soft Skills & Operational Fit
The candidate's project descriptions highlight strong attention to data accuracy and the ability to translate raw figures into clear, decision-ready insights for stakeholders, indicating good communication and operational fit for a data-driven role. The diverse project portfolio suggests adaptability and a proactive learning attitude.