Aufgaben

About Us
At Mercedes-Benz, we don\xe2\x80\x99t just build cars, we create great software.
We develop tomorrow\xe2\x80\x99s mobility together in our product lines, pushing the digitization of the Mercedes-Benz Group AG forward. As a tech company, we are agile, innovative and always keep our finger on the pulse of change. This is why inner source, FOSS, secure code, DevOps, infrastructure as code (IaC), data analytics, artificial intelligence and many more are core components of our daily work.
IT is our passion, and we move towards a bright future, sometimes quickly, sometimes creatively \xe2\x80\x93 but always together.
We believe in integrity, trust and transparency as part of our flexible work culture and a strong emphasis on team work and the learning process, making mistakes and achieving continuous progress together in all products we create and develop within the Mercedes-Benz Group.
Our workplace is anywhere* with a fast connection either remotely at home, at your favorite caf\xc3\xa9 or at our integrated office at Wisma Mercedes-Benz that comes with an LRT station at its doorstep, our al fresco cafeteria, ample greenspaces and free parking.
Job Objectives

• Advising the customer on the use of AI, capturing the requirement and translating the requirement into suitable mathematical models
• Data preparation, exploratory data analysis and statistical analysis. Conception of labeling processes
• Selection, implementation and configuration of suitable (machine learning / semantic) models and procedures
• Conception and implementation of automated pipelines for model creation and quality assurance
• Conception and implementation of infrastructure and software to integrate AI into software products in a scalable way
• Ensuring compliance with applicable rules in the areas of data, AI and IT security compliance
• Documentation and communication of the procedures and results in a target group-adequate form

Qualifikationen

Experience (type of)

• Experience with Agile methodologies
• Experience with Enterprise technologies
• Experience with Product Oriented Teams
• Programming or Data Architecture related certification

Specific knowledge/ Skill

• Excellent communication skills
• Good customer service orientation
• Analytical, passionate and drives technology and product quality
• Result-oriented thinking and action

Technical:

• SQL/noSQL, Python or R, Spark, Apache libraries, Machine Learning algorithms, Cloud Computing and Data Architectures

Level 3:
Knowledge and Skills:

• Knows the most important, mathematical and formal methods with their strengths and weaknesses.
• Basics of data preparation, exploratory data analysis, data visualizations and statistical analysis.
• Ability to implement and configure specific machine learning / semantic models.
• Implements automated pipelines for model creation and quality assurance.
• Knowledge of the implementation of infrastructure and software to integrate AI into software products in a scalable way.
• Knowledge of applicable rules from the areas of data, AI and IT security compliance.
• Ability to document and communicate procedures and results.

Responsibility and Scope for decision-making:

• Independent development of components for data preparation, exploratory data analysis and statistical analysis. Conception of labeling processes.
• Independent development implementation and configuration of suitable models and procedures.
• Independent contributions to automated pipelines for model creation and quality assurance.
• Independent implementation of infrastructure and software to integrate AI into software products in a scalable way.

Level 4:
Knowledge and Skills:

• Has broad knowledge and in-depth experience in the application of relevant, mathematical and formal methods with their strengths and weaknesses.
• Uses the methods of data preparation, exploratory data analysis, data visualizations and statistical analysis.
• Ability to implement and configure the relevant machine learning / semantic models.
• Ability to design and implement automated pipelines for model creation and quality assurance.
• Knowledge of the conception and implementation of infrastructure and software to integrate AI into software products in a scalable way.
• Knowledge of applicable rules from the areas of data, AI and IT security compliance.
• Ability to document and communicate the procedures and results in target group-adequate form.

Responsibility and Scope for decision-making:

• Advising the client on the use of AI, capturing the requirement and translating the requirement into suitable mathematical models.
• Data preparation, exploratory data analysis and statistical analysis. Conception of labeling processes.
• Selection, implementation and configuration of suitable (machine learning / semantic) models and procedures.
• Conception and implementation of automated pipelines for model creation and quality assurance.
• To integrate the design and implementation of infrastructure and software and AI into software products in a scalable manner.

Level 5:
Knowledge and Skills:

• Has a broad and deep understanding of AI methods and their applicability in day-to-day operations.
• Possesses broad and constantly expanded knowledge of mathematical and formal methods with their strengths and weaknesses.
• Possesses broad and constantly expanded knowledge methods of data preparation, exploratory data analysis, data visualizations and statistical analysis.
• Ability to implement and configure machine learning / semantic models close to the state of research.
• Ability to design and implement automated pipelines for model creation and quality assurance.
• Knowledge of the conception and implementation of infrastructure to integrate software and AI into software products in a scalable way.
• Knowledge of applicable rules from the areas of data, AI and IT security compliance.
• Ability to document and communicate the procedures and results in target group-adequate form.

Responsibility and Scope for decision-making:

• Independent consulting of the customer to identify new areas of application of AI.
• Independent consulting of the customer on the use of AI, on capturing the requirement and translating the requirement into suitable mathematical models.
• Independent data preparation, exploratory data analysis and statistical analysis, conception of labeling processes.
• Independent selection, implementation and configuration of suitable (machine learning / semantic) models and procedures, also on the basis of scientific literature.
• Conception and implementation of automated pipelines for model creation and quality assurance.
• To integrate the design and implementation of infrastructure and software and AI into software products in a scalable manner.
• Independent assurance of compliance with the relevant rules from the areas of data, AI and IT security.
• Networking and sharing of knowledge and experience within the company, within the Group and beyond.