Dublin, County Dublin, Ireland
Data Scientist with over 5 years of experience in data analysis, machine learning, and digital transformation. Proficient in Python, SQL, SAS, and data visualisation tools like PowerBI and Tableau. Skilled in developing predictive models, processing large datasets, and providing actionable insights to drive business decisions. Strong communicator, able to simplify complex data for stakeholders. Collaborative and results-focused, with a proven ability to work with cross-functional teams to develop strategies that improve customer engagement and optimise business processes. Seeking a full-time data science role to apply my expertise in predictive modelling and data-driven solutions.
- Conduct AI safety and security annotations for a major social media platform, ensuring compliance with data governance standards and improving model reliability. - Analyze and evaluate machine learning (ML) and deep learning (DL) model outputs , error messages, and generated content in a large-scale LLM project, leveraging Python and SQL to enhance accuracy, consistency, and operational efficiency. - Identify patterns in LLM behavior , diagnose model biases, and provide detailed feedback to optimize performance and mitigate risks. - Collaborate with cross-functional teams to refine annotation guidelines, improving AI model training and evaluation.
Ad hoc analysis: - Collaborated with retail banking teams to identify business problems, performed ETL (Extract, Transform, Load) processes using SQL and SAS, and integrated data-driven approaches into business strategies, converting insights into actionable solutions for various aspects of the banking field. - Presented concise reports and visualizations, offering data-driven solutions to the BOM for strategic decision-making. - Monitored and analyzed customer retention rates by product segmentation (credit card, lending, deposit, and digital product) to enhance overall customer experience. - Tracked Monthly Active Users (MAU) and studied user behavior patterns and cohorts to optimize the mobile banking app's UI/UX, boosting user engagement and retention. Project Execution and Presentation: - Collaborated with relevant stakeholders to identify business problems and define objectives. - Conducted data analysis and executed ETL processes using SQL, SAS, and Python, devising model schemes to address business challenges. - Developed and implemented statistical and machine learning models, providing actionable insights for personalized targeted marketing strategies. - Prepared insightful reports and delivered findings to BOM and stakeholders. - Strategically deployed, meticulously monitored, and iteratively improved models for ongoing optimization.
Monitored PQR metrics, including customer repayment performance by vintage and product, and tracked delinquency rate and recovery rate metrics for effective risk management assessment. Managed portfolio assets and tracked principal outstanding flow using robust recovery rate methodologies aligned with International Financial Reporting Standards (IFRS). Implemented robust reporting systems using SQL and Python, driving automation for heightened efficiency and timely report delivery. Strategized and executed plans while conducting in-depth ad-hoc analyses to tackle challenges in Consumer Lending Risk Department. Collaborated with outsourcing partners to implement performance management initiatives aligned with organizational objectives.