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Data Analyst with less than a year in fraud detection, risk modeling, and customer segmentation.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Analyst with hands-on project experience in fraud detection, credit risk modeling, and customer segmentation. Built predictive models on datasets exceeding 6M records, achieving AUC 0.988. Completed JPMorgan Chase Quantitative Research Virtual Experience - developing natural gas pricing models and probability of default systems. Strong foundation in SQL, Python, and Tableau with the ability to translate complex data into actionable business insights.
Great Lakes Executive Learning
Post Graduate Diploma · Data Science
February 1, 2024 – April 1, 2026
JS College
Bachelor of Business Administration (BBA) · Operations Management
March 1, 2023 – Present
Payment Fraud Detection Analysis
June 24, 2026 – Present
Analyzed 6.3M+ financial transactions to detect fraud patterns and high-risk transaction behaviors across multiple channels. Identified that TRANSFER and CASH_OUT transactions accounted for the majority of fraudulent activity, enabling targeted fraud monitoring. Built a logistic regression fraud detection model achieving 0.988 AUC across 6 fraud categories, demonstrating high predictive accuracy. Discovered balance inconsistencies and transaction chaining patterns, revealing potential intermediary money laundering accounts. Conducted statistical hypothesis testing (T-test & Chi-Square) confirming significant relationships between transaction type, amount, and fraud probability. Developed Tableau dashboards exposing fraud concentration patterns and behavioral anomalies across millions of records.
View ProjectOnline Retail Sales Analysis
June 24, 2026 – Present
Processed and transformed retail transaction data spanning 38 countries and 500K+ records to build a structured sales analysis dataset. Engineered revenue metrics and return indicators enabling product profitability and return rate analysis across 4,000+ SKUs. Identified top-performing products and highest-value customers, uncovering revenue concentration in a small subset of SKUs. Conducted monthly sales trend analysis highlighting seasonal revenue spikes and underperforming product categories. Built SQL-based analytical queries enabling rapid breakdown of sales performance by country, product, and time period.
View ProjectCustomer Segmentation using RFM & K-Means Clustering
June 24, 2026 – Present
Analyzed ~540K e-commerce transactions from 4,300 customers to quantify purchasing behavior and lifetime value potential. Engineered RFM (Recency, Frequency, Monetary) behavioral metrics to measure customer engagement and revenue contribution. Applied K-Means clustering to segment customers into 4 behavioral groups: VIP, Loyal, At-Risk, and Low-Value. Identified a high-value VIP segment (~8% of customers) generating a disproportionate share of total revenue, highlighting retention opportunities. Validated clustering performance using the Elbow Method and feature scaling, improving segmentation stability and interpretability.
View ProjectQuantitative Research Virtual Experience
JPMorgan Chase & Co. - Forage
April 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong alignment with the target role of Data Analyst, covering diverse analytical areas such as finance (fraud detection), retail sales, and customer behavior. The breadth of skills (SQL, Python, Tableau, statistical methods, machine learning) indicates a versatile and adaptable individual. The ongoing Post Graduate Diploma in Data Science further reinforces a commitment to continuous learning and professional development, which aligns well with a growth-oriented culture. However, the lack of professional experience means cultural fit is primarily inferred from project diversity and academic pursuits.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and an ability to translate complex data into actionable business insights. The focus on identifying patterns, optimizing processes, and quantifying business impact suggests a good operational fit for a data analyst role. However, without direct work experience, the ability to collaborate in a team and handle workplace stress is not directly observable.