Data Engineer (m/f/d)

at  Urban Software Institute GmbH

REQUIREMENTS:

• Bachelor’s or Master’s degree in Computer Science, Business Informatics, Statistics, or related field.
• Experience in the application of Machine Learning techniques and concepts, including the design and implementation of algorithms and predictive models.
• Proficiency in Python and experience using data science libraries (Pandas, Numpy, Scikit-learn, Pytorch) and tools (Jupyter notebooks, Apache Hadoop, Apache Airflow).
• Experience with the MLflow platform for managing the machine learning lifecycle.
• Knowledge of the entire lifecycle of a machine learning project, from data collection and cleaning to model training, evaluation, and deployment.
• Familiarity with floating car data is preferred.
• Excellent problem-solving abilities and intellectual curiosity.
• Strong communication skills, with the ability to explain complex concepts to a non-technical audience.

ROLE SUMMARY:

We are seeking a talented and driven Data Engineer to join our team, specializing in traffic, mobility, and environmental projects. This role will balance both research and commercial projects, and contribute to the development of new products and solutions, leveraging their technical expertise and creative problem-solving skills. The successful candidate will manage the entire lifecycle of a machine learning project - from data gathering and cleaning, to training and evaluating models, to final deployment.

RESPONSIBILITIES:

• Conduct comprehensive data analysis and modeling on large datasets to answer customer and research questions and inform strategic decision-making.
• Participate in the ideation, design, and implementation stages of product development, bringing a data-informed perspective to drive success.
• Develop, test, and maintain clean, efficient, and well-documented code using Python and associated data science libraries (e.g., Pandas, Numpy, Scikit-learn, Pytorch, MLflow) and tools (e.g., Jupyter notebooks, Apache Hadoop, Apache Airflow).
• Drive the collection, cleaning, and transformation of raw data into actionable insights for both research and commercial purposes.
• Evaluate the performance of machine learning models, optimize them for both accuracy and efficiency, and ensure they are fit for purpose for both research and commercial projects.
• Deploy machine learning solutions into production, monitor their performance, and iterate and improve as needed.
• Communicate complex data findings and insights effectively to non-technical stakeholders.
• Stay current with emerging tools, techniques, and technologies in the field of data science.
• Collaborate with cross-functional teams to understand needs and devise possible solutions.