Associate: Simplify process integration via machine learning (temp. 6 months)

at  SAP

Requisition ID: 223652
Work Area: Solution and Product Management
Location: Walldorf/St. Leon-Rot
Expected Travel: 0 - 10%
Career Status: Student
Employment Type: Limited Part Time

COMPANY DESCRIPTION

SAP started in 1972 as a team of five colleagues with a desire to do something new. Together, they changed enterprise software and reinvented how business was done. Today, as a market leader in enterprise application software, we remain true to our roots. That’s why we engineer solutions to fuel innovation, foster equality and spread opportunity for our employees and customers across borders and cultures.
SAP values the entrepreneurial spirit, fostering creativity and building lasting relationships with our employees. We know that a diverse and inclusive workforce keeps us competitive and provides opportunities for all. We believe that together we can transform industries, grow economics, lift up societies and sustain our environment. Because it’s the best-run businesses that make the world run better and improve people’s lives.

EDUCATION AND QUALIFICATIONS / SKILLS AND COMPETENCIES

• Graduate of a university or a university of applied sciences
• Preferred fields of studies: computer sciences, machine learning and integration of business processes
• Language skills: English and German
• Soft skills: Good presentation skills
  Your set of application documents should contain a cover letter, a resume in table form, school leaving certificates, copies of any academic degrees already earned, and if available, references from former employers (including internships). Please describe as well your experience and skills in foreign languages and computer programs/ programming languages.

WORK EXPERIENCE

Basic understanding of key integration use cases, such as API based integration, integration of applications, integration with business partners and government agencies, integration of events and IoT (Internet of Things) devices would be helpful. Furthermore, machine learning skills are essential.

PURPOSE AND OBJECTIVES

SAP Cloud Platform Integration Suite is SAP’s strategic platform as a service for the integration of business processes between different applications. These applications can be from SAP or non-SAP, they can be installed in an on-premise landscape within an enterprise, they can run at a business partner, or they can run in the cloud. In order to design and run the integration scenarios between these different applications, one has to define the message flow between the various applications and the required steps to successfully integrate the systems with each other, e. g. mapping of source and target interfaces and conversion of communication protocols. Traditionally the configuration of such integration scenarios is done manually, which results in high efforts to create and maintain these integration scenarios. In this thesis it should be evaluated how machine learning methods can be leveraged in order to simplify he manual administration, maintenance and monitoring of integration scenarios. As one example, machine learning algorithms support the user in the creation of an integration scenario via a proposal service based on already existing integration scenarios.

EXPECTATIONS AND TASKS

As part of this thesis it needs to be evaluated in which areas of the administration, design, configuration and monitoring of integration scenarios machine learning methods can be leveraged in order to simplify the integration between different applications. Potential areas of investigations to leverage machine learning can include for example:

• Propose integration content based on all the products which a customer has subscribed to
• Auto-complete function for configuration of parameters.
• Suggest API (Application Programming Interface) policies to be applied for similar APIs
• Detect slow performances in processing of messages, recommend best practices adapted by similar patterns
• Detect instability of runtime nodes and auto startup nodes based on current demand.