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Machine Learning Engineer

hace 2 meses

Madrid, Madrid, España Kapsch Trafficcom Transportation Sa. A tiempo completo
About the Role

We are seeking a highly skilled Machine Learning Engineer to join our team at Kapsch Trafficcom Transportation Sa. in Madrid. As a key member of our Mobility Data Platform team, you will play a crucial role in designing and building machine learning services to power our data products, enabling data-driven decisions to improve traffic safety and prediction accuracy.

Key Responsibilities
  • Design and develop machine learning services to analyze and process large datasets, enabling data-driven decision making.
  • Develop, train, and test machine learning models to perform information retrieval and forecast analysis for mobility and traffic data.
  • Analyze use cases of machine learning algorithms, ranking by success probability and applying the outcome to support business decisions.
  • Enrich existing machine learning frameworks and libraries to improve model performance and efficiency.
  • Productize training models and package them into independent software services.
  • Continuously improve deployed models by applying state-of-the-art techniques and ensuring code and data set quality.
Requirements
  • Bachelor's degree in Computer Science or related field (or equivalent practical experience).
  • Experience in product development of machine learning-related products and solutions.
  • Solid understanding of machine learning algorithms, proficient in math and statistics.
  • Proficient experience with programming languages and data tools, including Python, NumPy, Pandas, Matplotlib, scikit-learn, and model/code testing.
  • Hands-on experience with building machine learning train and release pipelines.
  • Analytical mindset and solution-driven personality.
  • Good oral and written English communication skills.
  • Knowledge of time series and geo-data analysis.
  • Experience with TensorFlow, Keras, R, Jupyter, and data version control.
  • Practical knowledge of CI/CD tools.
What We Offer
  • Permanent role with a hybrid working model (3 days of remote work/week).