learn more about LexisNexis Risk at the link below, risk.lexisnexis.com About our Team: You will part of a small team assisting the business with statisticalanalysis and building predictive models for credit, fraud, and risk. About the Role: We are looking for a Data Scientist to conduct statisticalanalysis and build predictive models for credit, fraud, and risk. The ideal candidate will have experience in data mining, statistical methods, and modelling/scoring techniques. They will balance day-to-day analytics assignments, research experiments and will contribute to the advancement of the global data science … group. Responsibilities Building and testing credit and fraud risk statistical models, consulting in support of existing and new customer sales Providing complex analytical results in clear, simple messaging to evidence the value provided by our products Following modelling best practices and provide feedback on ways to enhance current processes more »
models.Key Tasks: Fraud Detection: Access existing systems, evaluate vendor models, and create a roadmap for system improvements aimed at fraud prevention. Pattern and Irregularity Analysis: Use statistical tools to uncover patterns and irregularities in data that could indicate fraudulent activities. Predictive Modelling: Employ predictive modelling techniques to identify … Cross-Department Collaboration: Work closely with other departments to enhance overall security and fraud detection.Requirements: Programming Proficiency: Fluency in Python with deep knowledge of statistical packages and ML/DL libraries/frameworks (e.g., Scikit-learn, NumPy, Keras/TensorFlow/PyTorch) and visualization libraries (e.g., Matplotlib, Plotly, Seaborn … . Database Skills: Fluency in SQL and familiarity with data visualization tools (e.g., DataStudio, Tableau). StatisticalAnalysis: Basic understanding of statistical analysis. Growth Mindset: Proactive and enthusiastic about keeping up-to-date with the latest technologies and researching new ideas. Commercial Experience: Experience in implementing production more »
e.g., Scikit-learn, TensorFlow) and visualization libraries (e.g., Matplotlib, Seaborn). - Strong SQL skills and experience with data visualization tools (e.g., Tableau). - Basic statisticalanalysis knowledge. - Experience in deploying production ML systems. - Familiarity with DevOps in a data science context (e.g., MLOps) is a plus. Tools currently more »
e.g., Scikit-learn, TensorFlow) and visualization libraries (e.g., Matplotlib, Seaborn). - Strong SQL skills and experience with data visualization tools (e.g., Tableau). - Basic statisticalanalysis knowledge. - Experience in deploying production ML systems. - Familiarity with DevOps in a data science context (e.g., MLOps) is a plus. Tools currently more »
