(Senior) Data Scientist

Key Skills

Java Programming, Analytical and Problem solving, Management, Python Programming, SQL, Git, Aartificial intelligence, Machine learning techniques, Jenkins, Deep Learning, XGBoost, Apache Airflow, Continuous Integration & Continuous Delivery - CI/CD, LoadRunner, Github, Databricks, Data Engineering, Data pipelines, Mathematics, Random forests, Data Analysis

Job Description

Are you thinking of next steps in your career path? You deserve a company that really cares about its talent!

At Allianz Technology, we are looking for a Senior Data Scientist who is willing to be part of an international team of Data Scientists.

You will have the opportunity to contribute and shape different data products for Data Science, Machine Learning and Artificial Intelligence along the insurance value chain, e.g., chasing fraudsters, developing voicebots, working on the analysis of structured data such as financial data and unstructured data such as texts and images.

KEY RESPONSIBILITIES

• Contribute to the success of Allianz Technology transforming Allianz into a data driven company
• Develop end-to-end solution blueprints for our international stakeholders, including data engineering, machine learning and artificial intelligence algorithms or custom algorithm development
• Investigate new data technologies and contribute to the continuous development of the Applied Data Science team by suppoting to growth of AI services
• Contribute and shape the future of data science at Allianz, by developing and distributing AI products
• Identify opportunities for innovation through analytics in the business of Allianz

KEY REQUIREMENTS

•  At least 3+ years of experience
• Excellent degree (PhD/Master) in computer science, natural science, mathematics, engineering or similar degree to data processing
• Proficient in cloud-based ML-solutions (E.g. Azure) and languages (e.g. Python, SQL, R) as well as analytics platforms & tools (e.g. Databricks, Github runner/Jenkins, Airflow)
• Excellent understanding of machine learning techniques and algorithms, (e.g., Random forest, XGBoost, deep learning)
• Experience on developing and deploying end-to-end models
• Experience with docker and kubernetes cluster and versioning control tools (e.g., git, and building CI/CD pipelines) 
• Experience with Mlops tools, e.g. mlflow
• Well-developed analytical, logical and problem solving skills
• Advanced ability to perform exploratory data analysis and visualize data
• Familiarity with data management tools
• Excellent communication and presentation skills in English (vehicular language)