Data Scientist

2 weeks ago


Dubai, United Arab Emirates Siemens Full time

Data Scientist - UAE / Dubai

Siemens strongly believes in the value of a Digital Portfolio, hence Smart Infrastructure combines Digital Power Distribution and Digital Building Infrastructure technologies. Our Digital Portfolio will enable our customers to enjoy occupant’s intuitive buildings which are comfortable, safe, secure and energy efficient.

Smart infrastructure from Siemens intelligently connects energy systems, buildings and industries to adapt and evolve the way we live and work. We work together with customers and partners to build an ecosystem that intuitively responds to the needs of people and helps customers to better use resources. It helps our customers to thrive, communities to progress and supports sustainable development.

Join our Smart Infrastructure Energy and Performance Services division as Data Scientist and help us re-imagine the world by finding solutions and making the world a smarter place for tomorrow.

Your Role - International, Challenging And Future-Oriented

We are looking for a Data Scientist to transform our services to and in the digital metaverse. The Data Scientist is responsible for the design, implementation and upkeepment of digital platforms that continuously analyze data for actionable intelligence that corrects and enhances field performance.

To be a successful Data Scientist, you must be able to work on engineering projects in the mechanical & electrical domain and have great communication and presentation skills.

Data Scientist Responsibilities:

- Design, build and operate platform that moves services to the digital metaverse.
- Build algorithms to analyze data based on engineering know-how that will be provided by specialist functions for actionable intelligence.
- Developing deep understanding of the industry and develop platform that delivers key insights by analyzing data
- Forecasting different fundamentals such as energy performance and predictive maintenance
- Build and develop new or existing data infrastructure
- Perform data quality assessment, data stitching, cleaning, and visualization for customers’ datasets.
- Build prototype solutions addressing customer pain points based on machine learning, artificial intelligence and optimization techniques in a fast pace, agile environment.
- Evaluate developed solutions using representative meaningful metrics.
- Develop robust, reliable algorithms & solutions for the identified pain points through close cooperation with multi-disciplinary team members.
- Deploy the developed solutions as part of a larger ecosystem as agreed amongst the team members.
- Communicate findings to both technical and non-technical audience.

Your Qualifications And Skills - Digital And Solid
- Bachelor’s or Master’s degree in Data Science, STEM, AI, ML and similar field.
- Bachelor’s degree in engineering discipline would be an advantage.
- 5+ years of hands-on experience building AI/ML solutions using real data.
- Concrete understanding of statistics, optimization, ML and AI.
- Strong in Python programming language and its libraries
- Knowledge of GUI-Python tools.
- Strong skills in data communication APIs such as REST, Java based interfaces.
- Strong skills in handling databases such as SQL etc.
- Strong skills in data visualization.
- Concrete knowledge in cloud computing - AWS and Azure services
- Ability to handle big data.
- Excellent presentation and interpersonal skills.

What else do you need to know?

As a world leader in developing and producing the most advanced engineering technologies, we improve lives and further human achievements worldwide, while also protecting the climate - all thanks to our employees. Working with us, you have the foundation to develop personally and professionally. We give you the chance to do something significant, that benefits society and human progress. We give you the chance to create a difference.

**Organization**: Smart Infrastructure

**Company**: Siemens Industrial LLC

**Experience Level**: Experienced Professional

**Full / Part time**: Full-time


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