London, England, United Kingdom
Data Scientist focusing on Fraud Detection for LexisNexis Risk Solutions in London. Past experience (5 years+) in the Insurance and Health sector in Paris. Strong dual background in Computer Science and Marketing & Management.
Key responsibilities and achievements: - Project managed a 3-person data science team, delivering 15+ projects in 2024 (+35% YoY). - Developed an OCR model in Python, tripling recovery from fraudulent claim documents. - Delivered post-merger BI report, validating investment strategies with consumption insights. - Used statistic modelling and EDA to improve personalised insurance quotes & compliance. - Socio-economic profiling (statistical analysis, income imputation) for targeted retirement. - Explored generative AI, synthetic data, anonymisation techniques for data governance and GDPR compliance.
Key responsibilities and achievements: - Optimised agency network with geospatial analysis (Python, GIS); reducing travel times and costs. - Applied ML clustering and GIS to optimise teleconsultation sites, improving healthcare access. - Conducted housing/care synergy analysis via ML & GIS for strategic decision-making. - Internalised geomarketing studies, saving £30K/study and deepening market understanding. - Created an ARIMA Time Series Forecasting model for sick-leave management product.
Key responsibilities and achievements: - Standardised geospatial databases (Shapefile, GeoJSON) via Geopandas, Shapely and ETL pipelines. - Developed an internal address geolocation REST API (Python, Flask).
Key responsibilities and achievements: - Conducted research on anonymisation metrics and solutions to protect sensitive data. - Enhanced QAR algorithm (R, Python) via code parallelisation. - Evaluated data anonymisation metrics for AFNOR (K-Anonymity, L-Diversity, Power BI) on big data.
Tutored high school students in mathematics.