Expires soon SAP

(Associate) Data Scientist - Deep Learning Center of Excellence Job

  • Berlin, Germany
  • Studies / Statistics / Data

Job description

Requisition ID: 149888 
Work Area: Software-Design and Development 
Location: Berlin 
Expected Travel: 0 - 30%  

 

COMPANY DESCRIPTION

As you know, SAP’s vision is to help the world run better and improve people’s lives.

As THE cloud company powered by SAP HANA®, SAP is a market leader in enterprise application software, helping companies of all sizes and industries Run Simple.

We empower people and organizations to work together more efficiently and use business insight more effectively. SAP applications and services enable our customers to operate profitably, adapt continuously, and grow sustainably.

At SAP, we believe in the power of collaboration and empower our employees to perform at their best in an environment that encourages free and open expression of ideas. Here, you work alongside creative thinkers who share your interests, while turning big ideas into reality for our customers. With innovative job training, mentors to help you grow, and the flexibility to balance your work and personal life, you’re able to build your career. It’s no wonder that some of the sharpest minds from around the world are working for a company that is consistently recognized as a global top employer.

 

 

As an (Associate) Data Scientist for the SAP Deep Learning Center of Excellence you will work within a team of machine learning experts and developers solving real-world problems and building new, cloud based machine learning solutions and services. Our use cases span commerce, finance, procurement, logistics, and travel. You will work against fixed timelines and your success will be measured by the ability to deliver production-grade machine learning models reaching accuracies on par with top notch research prototypes.

Desired profile

EXPECTATIONS AND TASKS

·  Build Machine Learning models to solve real problems working with real data
·  Understand and implement most recent machine learning algorithms and approaches
·  Ensure accuracy and system scalability through continuous evaluation, analysis, and refinement
·  Fulfill both functional and non-functional (availability, throughput, latency) requirements

 

 

EDUCATION AND QUALIFICATIONS / SKILLS AND COMPETENCIES

You are a self-starter, goal-oriented, striving for perfection engineer. As an ideal candidate you will demonstrate a wish to build a track record in development of large-scale machine learning pipelines and combine it with curiosity for exploring of new use cases and applications of machine learning to domains spanning processing of structured and unstructured data.

 

Required skills & qualifications:

·  Master's degree in Machine Learning, Computer Science, Physics or related field
·  Ability to working with very large datasets in distributed Linux/Unix environments
·  Experience with Python and Machine Learning software packages such as scikit-learn or TensorFlow 
·  Experience with data and error analysis and ability to present core insights in a clear and compelling way
·  Team spirit and desire to overcome obstacles and make your work benefit SAP's customers
·  Excellent English language skills

 

WORK EXPERIENCE

0-5 years of relevant professional experience. Graduates are encouraged to apply.

The position can be filled as part time position.

 

SAP'S DIVERSITY COMMITMENT 

 

To harness the power of innovation, SAP invests in the development of its diverse employees. We aspire to leverage the qualities and appreciate the unique competencies that each person brings to the company.

 

SAP is committed to the principles of Equal Employment Opportunity and to providing reasonable accommodations to applicants with physical, sensory and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please contact us . Requests for reasonable accommodation will be considered on a case-by-case basis.

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