Data Scıentıst

Allianz Sigorta A.Ş

• Deep understanding of predictive analytics,
• Proficient in predictive analytics modelling tools such as Python, SAS Enterprise Miner, SPSS or R,
• Ability to develop customer segmentation models, propensity models, next best action model, customer lifetime value model,
• Knowledge in technical application/environment/architecture specialization,
• Ability to work creatively and analytically in a problem-solving environment,
• Ability to work in agile environment,
• Excellent communication (written and oral) and presentation skills,
• 3+ years of advanced analytics experience,
• Master’s or Ph.D. in relevant technical discipline preferred, bachelor's degree required,
• Experience with evolving approaches and technologies such as Big Data, Artificial Intelligence, Machine Learning,
• Expertise in applying statistical methods and outputs to create business value.

İŞ TANIMI

• Apply advanced analytical methods to understand customer behavior & insight, to investigate and develop alternative methods to use (in)directly in pricing, to perform advanced analysis which requires high level of data mining with multiple inputs from multiple sources,
• Use of advanced analytics techniques to recognize patterns and understand retention, propensity & claim behavior, develop alternative solutions for different business needs and make valuable discoveries leading to positive business outcomes,
• Explore structured and unstructured customer, claims, production, other external and market data from different data sources and convert raw data into formats that can be used by other tools,
• Develop and evaluate descriptive, diagnostics, and predictive analytics models that lead to optimal value extraction from the data,
• Own end-to-end management of analytical modeling projects; ensure quality, accuracy, relevance and consumption of analytical results and recommendations,
• Convert desired outcomes into value-based business initiatives, linking them to specific analytics, and applying interpretation methods/ techniques to create value,
• Manage model degradation/ensure models remain predictive,
• Perform ongoing monitoring of models’ quality to proactively identify required updates,
• Provide input and guidance on data visualization techniques.