Senior ML Data Scientist - Wireless People Sensing

Algorized; Etoy; On-site; CHF 120,000 - 180,000; Full time

Job summary

Algorized is seeking a Senior Data Scientist for an on-site role in Etoy, Switzerland. You will design and deploy ML models for wireless people sensing using radar and other sensor signals, collaborating with software engineers and MLOps to build robust products.

You will develop foundational models, evaluate time-series methods, and mentor junior teammates while advancing data-science practices across the company. A PhD and 3+ years of hands-on ML experience are required.

Qualifications

• PhD in Data Science, Computer Science, Wireless Communication, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
• At least 3 years hands-on experience developing machine-learning or data-science solutions for real-world applications.
• Strong knowledge of data science and ML with experience applying statistical and learning-based methods to sensor or time-series data.
• Strong understanding of signal-processing principles and practical experience with spectral analysis, filtering, estimation, detection, tracking or sensor fusion.
• Strong Python skills with practical experience using PyTorch, scikit-learn, NumPy, and SciPy.
• Ability to own open-ended technical problems and move from exploration to validated solutions.
• Excellent collaboration and communication skills, with enthusiasm for solving challenging people-sensing problems.

Responsibilities

• Design, develop, and improve ML models and algorithms for wireless people sensing using radar and other sensor signals.
• Develop foundational models to support multiple people-sensing tasks, environments, and sensor configurations.
• Create signal-processing, ML, and deep-learning methods to transform wireless sensor signals into robust outputs.
• Design model architectures and learning approaches that capture spatial and temporal patterns in sensor data.
• Plan experiments, define evaluation methods, and analyze model behavior and generalization.
• Evaluate advances in time-series and representation learning to improve robustness across environments.
• Collaborate with software engineers and MLOps on infrastructure development.
• Mentor junior team members and contribute to data-science practices.

Skills

Python

PyTorch

scikit-learn

NumPy

SciPy

Signal processing

Time-series

Ownership

Collaboration & communication

Education

PhD in Data Science/CS/Wireless Communications/EE/Applied Math/Physics

Tools

PyTorch

scikit-learn

NumPy

SciPy

Job description

Join Us in Building the Nervous System for Physical AI
Algorized is a VC-funded Silicon Valley deep-tech company with Swiss roots. We build edge-AI models that give robots real-time awareness of people using existing wireless sensors, enabling safer human-machine collaboration.

As we continue to scale, we are looking for a Senior Data Scientist who is passionate about innovation, applied research, and turning complex sensing challenges into robust products. If you thrive in a dynamic startup environment, take ownership, and enjoy working across data science, signal processing, and product development, we would love to meet you.

This is an on-site position based in Etoy, Switzerland. Fully remote arrangements are not available.

Responsibilities

• Design, develop, and improve machine-learning models and algorithms for wireless people sensing using radar and other sensor signals
• Play a key role in developing foundational models that can support multiple people-sensing tasks, environments, and sensor configurations
• Develop signal-processing, machine-learning, and deep-learning methods that transform wireless sensor signals into accurate and robust people-sensing outputs
• Design model architectures and learning approaches that capture spatial and temporal patterns in wireless sensor data
• Design experiments, define evaluation methodologies, and analyze model behavior, limitations, and generalization
• Evaluate and adapt relevant advances in time-series and representation learning to improve model accuracy, robustness, and generalization across environments and sensor configurations
• Collaborate cross-functionally with software engineers and MLOps on infrastructure development
• Mentor junior team members and contribute to the company's technical direction and data-science practices

Minimum Requirements

• PhD in Data Science, Computer Science, Wireless Communication, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field
• At least 3 years of hands-on experience developing machine-learning or data-science solutions for real-world applications
• Proven expertise in data science and machine learning, with experience applying statistical and learning-based methods to sensor or time-series data
• Strong knowledge of signal-processing principles and practical experience applying methods such as spectral analysis, filtering, estimation, detection, tracking, or sensor fusion
• Strong understanding of machine-learning fundamentals, including model development, evaluation, optimization, and generalization
• Strong Python skills and practical experience with frameworks such as PyTorch, scikit-learn, NumPy, and SciPy
• Ability to take ownership of open-ended technical problems and move effectively from exploration to validated solutions
• Excellent collaboration and communication skills, with genuine enthusiasm for solving challenging people-sensing problems

Preferred Requirements

• Experience with radar, RF sensing, LiDAR, computer vision, acoustics, or other sensing modalities
• Experience designing experiments and working with complex, noisy, or imperfect real-world data
• Familiarity with self-supervised learning, representation learning, multimodal models, or foundation-model development
• Experience with 3D positioning, occupancy sensing, human activity recognition, tracking systems, or vital-sign estimation
• Familiarity with edge-AI constraints and the trade-offs involved in moving models from research into production
• Experience collaborating across data science, embedded, software, and product teams

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.