Data Scientist & Engineer (f/m/d)
Deutsche Börse Group · Frankfurt, Hesse, Germany
- On site
- Full-time
- €75,000 / year
- Frankfurt, Hesse, Germany
Job highlights
- Develop AI/ML models for financial markets.
- Build and maintain scalable data pipelines.
- Analyze capital markets data extensively.
- Implement MLOps and collaborate with teams.
- Requires 3-5 years of experience.
About the role
Data Scientist & Engineer at Deutsche Börse Group
Join the Chief Technology Officer (CTO) area, the IT core of Deutsche Börse Group. We are responsible for group-wide IT infrastructure, data analytics, and enterprise architecture, driving digital transformation and innovation while ensuring operational stability.
About the Role
We are seeking an experienced Data Scientist & Engineer for our Group Data Services team. You will design, develop, and deploy data science solutions and data engineering pipelines to create business value. Working with capital markets data, you will build production-ready analytics, machine learning models, and scalable data products. Collaboration with business stakeholders, data engineers, and product teams at the intersection of advanced analytics and financial markets infrastructure is key.
Your Responsibilities:
- Design, develop, and deploy machine learning models and analytics solutions for capital markets use cases.
- Build and maintain scalable data pipelines using Databricks, Delta Lake, and related technologies.
- Analyze and model price data, order book data, and trading patterns from Eurex, Xetra, and 360T.
- Implement MLOps practices using MLflow for experiment tracking, model versioning, and deployment.
- Collaborate with business stakeholders to translate requirements into technical solutions.
- Support data quality and governance initiatives for market data products.
- Maintain comprehensive documentation for models and pipelines.
- Mentor junior team members and contribute to team best practices.
Your Profile:
- Master's degree in Data Science, Computer Science, Statistics, Mathematics, Quantitative Finance, or comparable field; alternatively, Bachelor's with substantial relevant experience.
- 3-5 years of professional experience in data science, machine learning, or data engineering.
- Strong proficiency in Python with data science libraries (pandas, NumPy, PySpark, scikit-learn).
- Solid experience with SQL and analytical databases.
- Experience with Apache Spark and large-scale data processing.
- Strong skills in time-series analysis and statistical methods.
- Familiarity with version control (Git/GitHub) and CI/CD workflows.
- Excellent communication skills; fluency in English required.
Highly Desirable:
- Hands-on experience with Databricks or similar cloud-based data lake house platforms.
- Experience working with financial market data.
- Understanding of capital markets concepts.
- Experience with MLflow and MLOps practices.
- German language skills.
Key skills/competency:
- Data Science
- Machine Learning
- Data Engineering
- Python
- SQL
- Apache Spark
- Databricks
- MLOps
- Financial Markets Data
- Time Series Analysis
Skills & topics
- Data Scientist
- Data Engineer
- Machine Learning
- Python
- SQL
- Apache Spark
- Databricks
- MLOps
- Capital Markets
- Financial Data
- Analytics
- Deutsche Börse Group
How to get hired
- Tailor your resume: Highlight your Python, SQL, Spark, and ML skills. Emphasize experience with financial data and Databricks.
- Showcase your projects: Include personal projects or contributions to open-source relevant to data science and engineering.
- Prepare for technical interviews: Practice coding challenges, SQL queries, and explain machine learning concepts.
- Understand the domain: Research capital markets, trading venues, and Deutsche Börse Group's role.
- Express your interest: Clearly articulate how your skills align with the job description during the application and interview process.
Technical preparation
Master Python, pandas, NumPy, and scikit-learn.,Practice SQL queries and database operations.,Build Spark pipelines for large datasets.,Familiarize with Databricks and MLflow.
Behavioral questions
Describe a complex data problem you solved.,How do you handle ambiguous stakeholder requirements?,Discuss a time you mentored a junior colleague.,How do you ensure data quality in pipelines?
Frequently asked questions
- What specific capital markets data will I work with as a Data Scientist & Engineer at Deutsche Börse Group?
- As a Data Scientist & Engineer at Deutsche Börse Group, you will work with various capital markets data, including price data, market data, and trading information from platforms like Eurex, Xetra, and 360T. This encompasses analyzing price data, order book data, and trading patterns.
- What are the core technologies used in the Group Data Services team at Deutsche Börse Group?
- The core technologies leveraged by the Group Data Services team include Databricks, Delta Lake, Apache Spark for large-scale data processing, Python with its data science libraries (pandas, NumPy, PySpark, scikit-learn), SQL, and MLflow for MLOps practices.
- Is prior experience with financial market data a strict requirement for the Data Scientist & Engineer role at Deutsche Börse Group?
- While hands-on experience with financial market data is highly desirable, it is not a strict requirement. A strong foundation in data science, machine learning, and data engineering, coupled with a willingness to learn about capital markets, can be sufficient. However, prior experience significantly strengthens your application.
- What is the expected level of collaboration with business stakeholders for this Data Scientist & Engineer position?
- Collaboration with business stakeholders is a key aspect of this role. You will work closely with them to understand requirements, translate them into technical data science and engineering solutions, and ensure the developed models and data products drive tangible business value for Deutsche Börse Group.
- Does Deutsche Börse Group offer opportunities for professional development in data science and engineering?
- Deutsche Börse Group emphasizes continuous improvement and professional development. As a Data Scientist & Engineer, you will have opportunities to mentor junior team members, contribute to team best practices, and work with cutting-edge technologies, fostering your growth in the field.
- What is the typical career path for a Data Scientist & Engineer at Deutsche Börse Group?
- A typical career path can involve deepening expertise in specific areas like machine learning or data engineering, moving into more senior or lead roles, or potentially transitioning into management positions within the Group Data Services or broader CTO area. Continuous learning and performance are key drivers.