Description

Wiliot was founded by the team that invented one of the technologies at the heart of 5G. Their next vision was to develop an IoT sticker, a computing element that can power itself by harvesting radio frequency energy, bringing connectivity and intelligence to everyday products and packaging, which were previously disconnected from the internet. This revolutionary combination of AI, cloud, and semiconductor technology is being utilized by some of the world’s largest consumer, retail, food, and pharmaceutical companies to transform the way products are created, distributed, sold, used, and recycled.

Our investors include Softbank, Amazon, Alibaba, Verizon, NTT DoCoMo, Qualcomm, and PepsiCo.

We are seeking a data scientist with proven hands-on skills to join our team and research and develop high-performing, scalable applications that leverage time-series, multi-modality sensors to generate actionable insights. You will collaborate with talented individuals from diverse disciplines to create strategic, impactful products. 

Responsibilities

• Conduct and lead experiments, including fieldwork, to gather data, build models, and provide valuable insights to our customers.
• Design algorithms and SW solutions.
• Work hands-on with the data using tools to extract, analyze, and visualize data.
• Collaborate with cross-functional stakeholders, including product, hardware, software engineering, and customers.

Requirements

• BSc in Computer Science, Electrical Engineering, or equivalent. 
• Strong background in signal processing.
• Background in experimentation and laboratory work.
• Experience with AI/ML/DL methods.
• At least 3 years of development experience. 

Advantages

• An MSc or higher degree.
• Coding experience in Python. 
• Experience with time domain and sensor signals.
• Experience with cloud data environments and big data ETL procedures
• Familiarity with some ML/data-processing frameworks and libraries (e.g., TensorFlow, Keras, PyTorch)
• Experience in applying and optimizing foundation models.

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