Data Scientist CV Example

Lead data science initiatives across customer analytics and supply chain optimisation for the UK's largest retailer, working with datasets containing 25M+ customer transactions weekly.

• Built advanced customer segmentation models using K-means clustering and RFM analysis, identifying 5 high-value customer segments that generated £2.3M in additional revenue within 6 months
• Developed machine learning demand forecasting models using XGBoost and LSTM networks, reducing food waste by 12% across 3,400+ stores and saving £8.5M annually
• Created personalised recommendation engine using collaborative filtering, increasing online basket size by 18% and customer retention by 22%
• Implemented A/B testing framework for promotional campaigns, optimising discount strategies and improving conversion rates by 31%
• Built real-time inventory management dashboard using Tableau, enabling proactive stock management and reducing out-of-stock incidents by 28%
• Led cross-functional workshops with marketing and operations teams, translating complex statistical findings into actionable business strategies
• Mentored 2 junior analysts on advanced Python techniques and machine learning model deployment using Docker and AWS

Performed comprehensive data analysis on retail banking products, working with transaction data from 15M+ customer accounts to support risk management and customer experience initiatives.

• Analysed daily transaction patterns and customer behaviour using SQL and Python, identifying £12M in potentially fraudulent transactions and reducing false positive alerts by 35%
• Built automated ETL pipeline using Apache Airflow and PostgreSQL, processing 2TB of daily transaction data and reducing manual reporting time by 20 hours per week
• Developed credit risk assessment models using logistic regression and random forests, improving loan approval accuracy by 23% while maintaining risk thresholds
• Created interactive Power BI dashboards for senior management, providing real-time insights into customer acquisition costs, lifetime value, and churn predictions
• Conducted statistical analysis on mobile banking adoption, identifying key demographic factors that increased app usage by 45% among target segments
• Collaborated with compliance team on regulatory reporting, ensuring GDPR compliance while maintaining data utility for analytical purposes
• Presented monthly findings to stakeholders across retail banking division, influencing product development decisions affecting 8M+ customers

Supported customer analytics team with network performance analysis and customer satisfaction initiatives across mobile and broadband services.

• Analysed network usage patterns from 2M+ mobile customers using SQL and Excel, identifying peak congestion areas and supporting infrastructure investment decisions worth £15M
• Built customer churn prediction model using logistic regression, achieving 78% accuracy and helping retain 12,000+ high-value customers
• Created automated reporting system for customer service metrics, reducing manual analysis time by 15 hours weekly
• Conducted market research analysis on competitor pricing strategies, informing tactical pricing decisions for business customers
• Supported A/B testing of customer communication campaigns, improving email open rates by 19% through data-driven content optimisation