Data Analyst with 1+ years in Product Analytics & Business Intelligence
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Highly analytical and detail-oriented Data Analyst with 1.5 years of experience in leveraging data to drive business growth and operational efficiency. Proven expertise in building real-time dashboards, conducting in-depth funnel and cohort analysis, and implementing data-driven recommendations across product, marketing, and operational domains. Proficient in SQL, Python, and various BI tools for data manipulation, analysis, and visualization, contributing to significant improvements in conversion rates, data accuracy, and user experience.
THAPAR INSTItute Of Engineering And TECHNOLOGY
B.E. · Electronics Engineering (Instrumentation and Control)
August 1, 2021 – June 30, 2025
ENGLISH BHASHI
Data Analyst Intern
February 1, 2026 – June 1, 2026
India
THE FUTURE UNIVERSITY, MOHALI
Entrepreneur in Residence (Analytics)
July 1, 2025 – December 1, 2025
Mohali, Punjab, India
VENTURE LAB, TIET, PATIALA
Data Analyst intern
January 1, 2025 – June 1, 2025
Patiala, Punjab, India
Data Analytics Customer Segmentation
June 1, 2026 – Present
Developed an RFM (Recency, Frequency, Monetary) segmentation model on 5,000+ customer records, identifying 8 distinct customer segments and enabling targeted marketing strategies projected to increase revenue by 15%. Performed exploratory data analysis using Python (Pandas, NumPy, Matplotlib, Seaborn) to uncover purchasing patterns, seasonal trends, and high-value customer characteristics across 12 months of transaction data. Applied K-means clustering to validate RFM segments and identify additional behavioural patterns among customer cohorts.
E- Commerce ANALYTICS PROJECT
June 1, 2026 – Present
Designed and executed 25+ complex analytical queries on a multi-table e-commerce database to answer product, marketing, and business questions for a hypothetical online retailer. Analysed user behaviour across the entire customer lifecycle: acquisition, activation, engagement, retention, and revenue using multi-table joins across orders, sessions, page_views, products, and attribution tables. Built session-level and customer-level metrics, including time-to-activation, funnel conversion rates, cohort retention curves, cart abandonment analysis, repeat purchase intervals, and customer lifetime value (LTV). Delivered insights on A/B test results, channel attribution, product performance, and seasonal trends to simulate real-world analytics workflows.
Cultural Fit Analysis
The candidate's project diversity, ranging from e-commerce analytics to incubator analysis and customer segmentation, indicates adaptability and a broad interest in applying data analysis across different domains. Their experience in both internship and 'Entrepreneur in Residence' roles suggests a proactive and results-oriented mindset. The use of various tools and techniques aligns well with a dynamic data-driven environment. The academic projects further demonstrate initiative and a foundational understanding of core data analysis principles.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in identifying key drop-off points and improving conversion rates. Their experience in creating self-serve analytics layers indicates a proactive approach to empowering stakeholders. The descriptions suggest an ability to work independently and deliver tangible business impact. However, without direct interview data, assessing stress handling, and team collaboration is difficult.