Senior Data Scientist (Machine Learning / Deep Learning)

About the Company

 European deep-tech company developing innovative AI solutions at the intersection of sensor technology, data science, and machine learning. Our products transform complex data from physical sensors into accurate analytical insights for real-time decision-making across multiple industries, including environmental monitoring, product quality control, and applied research.

The team works at the intersection of artificial intelligence, signal processing, and experimental analytics, building advanced models for pattern recognition and predictive analysis based on complex datasets.

Responsibilities

-Process, clean, and validate data for building ML/DL models.

-Design, implement, and optimize Machine Learning and Deep earning models.

-Conduct experiments including training, testing, fine-tuning, and hyperparameter optimization.

-Analyze results and present insights clearly to the team.

-Improve data collection and preprocessing pipelines.

-Collaborate closely with the ML Lead and engineering team.

-Participate in selecting modeling approaches and experimental strategies.

-Work with sensor-generated and multidimensional datasets.

Requirements

-5+ years of experience in Data Science / Machine Learning / Deep Learning.

-Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related field.

-Commercial experience building and deploying ML/DL models.

Strong understanding of algorithms:

-Classification and regression

-Clustering

-CNN and fully connected neural networks

-Time-series analysis

-Experience working with databases (SQL or solutions from Oracle Corporation).

Practical experience with:

-Python

-PyTorch or TensorFlow

-scikit-learn

-NumPy, Pandas

-Experience training models in cloud environments (e.g., Amazon Web Services).

-Experience working in a Linux environment.

-Experience using Git.

-English level sufficient for technical communication.

Nice to Have

-Experience working with sensor or experimental datasets.

-Understanding of signal processing or pattern recognition.

-Experience building experimental ML pipelines.