Data Scientist

Expect more. Connect more. Be more at Diebold Nixdorf.  Our teams automate, digitize, and transform the way more than 75 million people around the globe bank and shop in this hyper-connected, consumer-centric world. Join us in connecting people to commerce in this vital, rewarding role.

Builds, deploys and supports the data and analytics infrastructure and tools required for scaling DN's expanding business. Designs, constructs and optimizes data management, database and related infrastructure and systems to meet varied and diverse business needs. Designs how data is stored, consumed, integrated and managed by different entities and digital systems. Collaborates with data consumers to determine, create and populate optimal data architectures, structures and systems. Plays a key role in selecting and configuring backend database technologies as required by business users.

• →Explore and analyze data to generate actionable insights.
• →Collaborate with business stakeholders, data engineers, and analytics teams.
• →Translate business needs into clear technical requirements.
• →Ensure high‑quality analytical outputs.
• →Structure, clean, and document datasets for reliable analysis.

• Diploma or equivalent work experience required.
• Minimum of 2-4 years of relevant experience or equivalent combination of education and experience in Data Engineering.
• Good business English skills (Written and spoken).

#LI-PS1

• 3 years of experience in data analysis or business analysis roles.
• Understanding of data and database concepts
• Strong proficiency in Python and SQL
• Experience in ETL/ELT processes or tools.
• Strong communication, attention to detail, and problem‑solving skills.

• Experience in CI/CD and agile working concepts.
• Experience in Azure DevOps
• Experience in PySpark
• Exposure to Azure cloud data platforms
• Prompt engineering

• Data Analytics – Strong understanding of analytical techniques as well as data structures and content.
• Analytical Thinking – Ability to break down problems and validate assumptions with data.
• Stakeholder Collaboration – Works well with diverse roles; asks the right questions.
• Documentation & Detail Orientation – Clear, structured writing; accuracy in specs.
• Adaptability – Comfortable working across multiple data domains and projects.

• Business requirement collection and translation into technical requirements
• Data Analysis using descriptive statistics
• Data, Extraction, Transformation and loading as prerequisites for data analytic solutions
• Hypothesis testing
• Quality checks and test cases

• High quality and clarity of analysis results
• Positive feedback from business and technical teams
• Independent and motivated personality