WHAT YOU BRING:

• University degree in a quantitative discipline (e.g., big data, data science, statistics, mathematics or econometrics, actuarial science, computer science or any related field)
• At least 5+ years of data science experience in a business environment
• Advanced knowledge of statistics and machine learning
• Excellent Python skills, including Tensorflow/Pytorch and other libraries/frameworks for machine learning
• Strong knowledge of SQL
• Knowledge of version control systems (Git is an advantage)
• Several years of experience in natural language processing (NLP) and GenAI models (eg. OpenAI, LLama)
• Experience with cloud-based systems (Microsoft Azure is an advantage)
• MLOps knowledge (eg. MLflow)
• Excellent data visulatisation and presentation skills, with the ability to present complex results understandably
• Business acumen and the abilkity to transform business problems into data-driven analytical tasks
• Basic familiarity with available AI tools such as ChatGPT

Why work with us? We offer the following benefits:Income:

• competitive salary
• bonus based on performance appraisal system
• voluntary pension fund contribution (supplement to individual contributions)
• discounted share purchase and free share options
• reduced premium for insurance products

ACKNOWLEDGEMENTS:

• our commitment to gender equality, i.e. equal opportunities and gender balance is certified by EDGE
• Allianz Hungaria employees believe that wellbeing, flexible working, an inclusive atmosphere and a reliable, stable workplace are the most important values, as demonstrated by the international Great Place to Work (GPTW) certification.
  Where is our office?
  1087 Budapest, Könyves Kálmán krt. 48-52

You’ll be part of our Advanced Analytics team, working together with data engineers, data scientists and business translators on solving differentiated business problems ranging from sales and marketing through claims handling to operations for a major insurance player in Central Europe. You’ll help create analytical solutions that range from designing, developing, and delivering code and models, to exploring meaningful insights, and translating them into actionable recommendations.

• Develop, fine-tune and deploy generative AI models, with particular focus on the application of NLP and large language models (LLM) for solving business problems
• Manage the full lifecycle of machine learning models: development, training, operation and maintenance
• Use cloud-based solutions and MLOps tools
• Create data visualization dashboards and reports to present model results in an understandable way for business decision-makers
• Close collaboration with business areas to assess needs and translate business problems into analytical solutions
• Keep track of new AI technologies and trends and actively participate in innovation initiatives