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Data Analyst with 1+ years in Data Science & Analytics
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MSc Data Science graduate (University of Surrey, UK, Merit) with 1+ year of analytical experience across customer retention, supply chain, and e-commerce domains. Skilled in SQL, Python, and Power BI; building dashboards, surfacing commercial insights, and presenting findings to business stakeholders. Strong foundation in statistical analysis and data modelling, with hands-on experience building and deploying interactive analytical tools.
University of Surrey, UK
MSc Data Science
August 1, 2024 – June 30, 2025
University of Kashmir, India
BTech Engineering
August 1, 2019 – June 30, 2023
Unified Mentor Pvt Ltd
Data Analyst
March 1, 2025 – Present
India
E-commerce Sales & Customer Analytics
November 1, 2025 – December 31, 2025
• Rebuilt 100,000+ flat, unstructured e-commerce transactions into a normalised PostgreSQL schema (5 tables, star schema) from scratch; implemented CTEs, window functions, and indexed queries for sales ranking, rolling averages, and cohort segmentation; enabling sub-second query performance on a previously unstructured source. • Engineered 4 net-new business metrics absent from source data (revenue, profit margin, AOV, purchase frequency); built a Power BI dashboard using DAX and star schema; surfacing £5.58M annual revenue, a £1.08M single-month peak, and 79% credit card transaction dominance, with drill-through from category to product level. • Identified a 6,000-customer churn spike in October; the period's transaction peak; coinciding with the end of a promotional period; reframed it as a preventable retention gap driven by post-promotion disengagement rather than organic attrition. • Applied Market Basket Analysis on transaction data; uncovered top cross-sell pairs; fashion accessories × children's fashion (lift 18.7) and garden tools × flowers (lift 22.6); translating association rules into ranked product placement and bundling recommendations.
Real Estate Price Forecasting & Recommendation Engine
October 1, 2025 – November 30, 2025
• Scraped and cleaned publicly available real estate listings; applied systematic feature engineering across 15+ features (room segmentation, property category encoding, outlier treatment); achieving ~50% MAE reduction vs mean predictor baseline (R2 = 0.90 on holdout set) across residential and commercial listing categories. • Built a complete end-to-end ML pipeline (web scraping → data cleaning → feature engineering → model training → deployment); delivered a live Streamlit application for real-time Indian property price prediction across residential and commercial segments. • Integrated a cosine similarity recommendation engine; ranking alternatives by price range, location, and property type; to surface relevant property options alongside each prediction, converting a single-output model into a full decision-support tool.
End-to-End Demand Forecasting System
July 1, 2025 – August 31, 2025
• Reduced demand forecast error by 36% via lag features, rolling statistics, and calendar encoding on XGBoost; built an ETL data pipeline and deployed a Streamlit forecast dashboard with adjustable horizon and scenario toggles. • Containerised the full pipeline (model, database, dashboard) using Docker Compose; optimised PostgreSQL ingestion via bulk COPY and indexed queries, reducing dashboard load time from ~30s to ~3s; enabling sub-5-second refresh on the monitoring interface.
5G Energy Consumption Prediction - MSc Dissertation
February 1, 2025 – July 31, 2025
• Built hybrid CNN-RNN models (RNN, LSTM, GRU) to forecast hourly energy consumption across 1,020 5G base stations on 92,629 records; achieved 44% reduction in forecast error (MAE: 1.52 to 0.85, MAPE: 3.3%) with consistent performance on unseen stations using group-stratified cross-validation. • Engineered 20+ features including temporal lags, spline time encodings, and signal smoothing filters; tuned with Optuna TPE; generalised consistently to unseen base stations, demonstrating potential for real-world energy monitoring and cost optimisation in 5G network operations.
SQL Advanced
HackerRank
January 1, 2024 – Present
Deep Learning Specialisation
iNeuron
January 1, 2024 – Present
Natural Language Processing
iNeuron
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic background (MSc Data Science) combined with practical project experience and a part-time Data Analyst role indicates a strong drive for continuous learning and application of knowledge. The diversity of projects (e-commerce, real estate, supply chain, 5G energy) suggests adaptability and a broad interest in data-driven problem-solving, which can contribute positively to a dynamic team environment. The focus on delivering tangible business insights and building end-to-end solutions aligns with a results-oriented culture. However, the limited professional experience (part-time during studies) might require some ramp-up in a full-time corporate setting.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, evidenced by identifying and reframing a customer churn spike and disproving an assumed shipping premium. Their ability to build interactive dashboards and present findings suggests good communication and stakeholder engagement. The project descriptions indicate a structured approach to data analysis and model development, which aligns with operational best practices. The part-time work experience during MSc studies shows initiative and a proactive approach to gaining practical experience.