Data Scientist (Python, Problem Solving , Machine Learning)

Daimler Trucks North America LLC; Puchong; On-site; MYR 80,000 - 120,000; Full time

Job summary

A leading company in the automotive sector is seeking a Data Scientist proficient in Python and machine learning methodologies. The role involves advising customers, developing AI solutions, and ensuring quality assurance. Required qualifications include a Bachelor's degree and a minimum of five years' experience in the field, along with solid knowledge in data analysis and a strong technical background.

Qualifications

• Minimum of 5 years’ experience in a related field required.
• Experience with Agile methodologies and Enterprise technologies is advantageous.
• Programming or Data Architecture certification preferred.

Responsibilities

• Advise customers on AI usage and translate requirements into mathematical models.
• Implementation of machine learning models and automated pipelines.
• Ensure compliance with data and IT security rules.

Skills

Analytical

Communication

Customer Service Orientation

Result-Oriented Thinking

Education

Bachelor’s Degree in Computer Science, Applied Mathematics, Engineering, or related field

Tools

SQL/noSQL

Python

R

Spark

Apache libraries

Machine Learning algorithms

Cloud Computing

Job description

Job Description - Data Scientist (Python, Problem Solving , Machine Learning) (MER0003L3Q)

Data Scientist (Python, Problem Solving , Machine Learning) Group : Mercedes-Benz Group AG

Description

Task Description:

• 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 labelling 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.

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.

Qualifications

Qualifications / Experience:

• Minimum of 5 years’ working experience in a related field is required.
• Bachelor’s Degree in Computer Science, Applied Mathematics, Engineering, or any other technology related field.
• 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
• 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.

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