JOB PURPOSE / ROLE
The Allianz Group is one of the world's leading insurers and asset managers with more than 88 million retail and corporate customers.
Allianz customers benefit from a broad range of personal and corporate insurance services, ranging from property, life and health insurance to assistance services to credit insurance and global business insurance.
Allianz is one of the world’s largest investors, managing over 660 billion euros on behalf of its insurance customers while our asset managers Allianz Global Investors and PIMCO manage an additional 1.
4 trillion euros of third-party assets. Thanks to our systematic integration of ecological and social criteria in our business processes and investment decisions, we hold the leading position for insurers in the Dow Jones Sustainability Index.
In 2017, over 140,000 employees in more than 70 countries achieved total revenue of 126 billion euros and an operating profit of 11 billion euros for the group.
You will be part of an international and multidisciplinary team of data scientists, data engineers, solution architects, and interaction designers.
We have a large portfolio of both operational and visionary analytic projects and are committed to using state-of-the-art open source technologies.
You will have the opportunity to contribute to and shape different data products for data science, machine learning, and artificial intelligence;
from translating business problems into analytical ones, to designing and implementing the entire data pipeline, including data engineering, exploration, training and validating ML and AI models, and prototyping direct and indirect action and interactive insights.
On every project you will be part of a cross-functional team that works in an agile, collaborative, and iterative manner.
Both quantitative reasoning and strong implementation skills are therefore highly important, and you should be comfortable working at the intersection of software engineering and quantitative research, including statistics, advanced machine learning, and artificial intelligence.
As a Data Scientist you will also interface with senior stakeholders and actively contribute to the project scoping. You will be in the center of the development and rolling-
out of AI solutions within the organization.
The position is based in Munich, Germany.
Conceive and develop end-to-end, data-driven solutions to support Allianz operational entities and initiatives.
Lead analytics products and data-driven initiatives with a small cross-functional team.
Develop and work on the integration of end-to-end software prototypes, including data engineering, machine learning and artificial intelligence algorithms, or custom algorithm development.
Strongly contribute in agile cross functional teams, bringing the development skills for robust integration of AI products into technical systems
Measure and track impact of the products developed through business KPIs. Present result of analyses to business owners and share knowledge with team members.
Support the simplification of processes so that business owners, in addition to data scientists, can leverage Big Data to drive business decisions.
Investigate new data technologies and contribute to the continual development of the Big Data architecture.
KEY REQUIREMENTS / SKILLS / EXPERIENCE
M.S. or Ph.D. in a relevant technical field such as computer science or engineering.
Strong programming experience in Python is required. Experience with R is a plus. Object oriented(java, C++) or functional (Scala) programming languages are a plus.
Fluency in English is required, additional languages are a plus.
Experience & Key Skills :
Experience with databases and SQL language.
Working experience as Data Scientist (1+ years).
Experience in leading projects and small, agile project teams is a strong plus.
Experience in maintaining ML services / API in production.
Software development experience is a strong plus.
Experience in manipulating and understanding complex, high-volume data from diverse sources.
Experience in applying modern machine learning and artificial intelligence models (supervised and unsupervised, such as gradient boosting and deep learning) in a business context.
Experience with distributed computing is a plus (e.g., Spark, Hadoop, H2O etc.).
Ability to relate business problems to data-driven solutions using machine learning and artificial intelligence.
Ability to communicate complex quantitative analysis in a clear and precise manner to audiences from different departments and at different levels.
Creative thinking and strong analytical and problem-solving skills.