Senior Data Scientist, Wireless Sensing & Edge AI

Algorized; Campbell (CA); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Algorized in Campbell, USA, is hiring a Senior Data Scientist to lead development of wireless people-sensing models from radar and sensor data. This hands-on role combines data science, signal processing, and product work in a hybrid or on-site setup.

You will own experiments, design architectures for time-series data, and collaborate with engineers and MLOps to translate research into robust, deployable capabilities. A PhD and 3+ years of relevant ML experience are expected.

Qualifications

• PhD in a quantitative field related to data science or engineering.
• At least 3 years of hands-on ML or data-science experience in real-world apps.
• Strong Python and ML fundamentals with sensor/time-series data experience.
• Solid signal-processing knowledge and methods (spectral analysis, filtering, tracking).
• Ability to own problems and drive from exploration to validated solutions.
• Excellent collaboration and communication skills.

Responsibilities

• Design, develop, and improve ML models for wireless people sensing.
• Develop foundational models supporting multiple tasks and sensor setups.
• Create signal-processing and ML methods to turn sensor data into robust outputs.
• Design architectures capturing spatial and temporal patterns.
• Plan experiments, evaluate methods, and analyze generalization.
• Collaborate with software engineers and MLOps for infrastructure.
• Mentor junior team members and shape data-science practices.

Skills

Python

PyTorch

Signal processing

Time-series analysis

Machine learning

Data science

Education

PhD in relevant field

Tools

PyTorch

scikit-learn

NumPy

SciPy

Job description

Algorized in Campbell, USA, is hiring a Senior Data Scientist to lead development of wireless people-sensing models from radar and sensor data. This hands-on role combines data science, signal processing, and product work in a hybrid or on-site setup.

You will own experiments, design architectures for time-series data, and collaborate with engineers and MLOps to translate research into robust, deployable capabilities. A PhD and 3+ years of relevant ML experience are expected.