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Data Science with 2+ years in Data Science, Machine Learning & Analytics.
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Data Scientist with 2 years of SQL, ETL, and reporting experience at BT, alongside an MSc in Data Science & Analytics (Distinction). Built an XGBoost churn model with SHAP interpretability, K-Means segmentation, and A/B testing across three independent projects. Right to work in the UK (Graduate Visa)
University of Hertfordshire, UK
MSc · Data Science & Analytics
August 1, 2023 – June 30, 2025
KMIT, India
BSc · Computer Science and Engineering
August 1, 2016 – June 30, 2020
Virtusa (Client: BT)
Data & Reporting Analyst
January 1, 2020 – December 31, 2022
India
CLV & Churn Risk Analysis - Subscription Business
June 1, 2026 – Present
Created customer value bands and identified that high-value customers drove 40% of total revenue loss. Applied K-Means clustering; found 25% of churn concentrated in the high-value segment due to unresolved support tickets, flagging as the priority group for targeted retention intervention. Designed a Power BI dashboard visualising cohort profiles, segment distributions, and CLV bands.
Customer Churn Prediction – Fintech Banking App
June 1, 2026 – Present
Built an XGBoost churn prediction model on 10K banking customers with class-weight balancing; improved recall from 0.19 (logistic regression baseline) to 0.81. Applied SHAP to interpret model predictions; identified age, inactivity, and geography as the top churn drivers and proposed targeted retention strategies.
Marketing Campaign Performance & A/B Testing
June 1, 2026 – Present
Analysed 15K campaign records via SQL to evaluate ROAS; identified Paid Social as the most scalable channel and 'Discount Offer' as the most revenue-efficient campaign type. Ran an A/B test in Python; found no statistically significant difference between ad variants, recommended channel budget reallocation over further creative iteration.
Google Analytics GA4
June 1, 2026 – Present
Google Data Analytics
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (fintech, subscription business, marketing) and academic background suggest adaptability and a broad interest in applying data science across different domains. The pursuit of an MSc while having prior work experience indicates a proactive and growth-oriented mindset, which aligns well with a culture of continuous improvement. The focus on practical applications in projects also suggests a results-driven approach.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through root-cause investigations and model building. Their ability to communicate complex findings to non-technical stakeholders is a valuable operational fit. The project descriptions suggest an outcome-oriented approach, focusing on business impact (e.g., improved recall, identified revenue loss drivers).