ML/Data Scientist

About the CompanyAlgorized is a fast-growing deep tech startup building software platform for people positioning and sensing. We leverage algorithms and edge-ML with any market available communication sensors such as Ultra-Wideband Radar for accurate people tracking, positioning, vital sign detection (breathing, heart-rate), age classifications.As we continue to rapidly expand, we are seeking a highly skilled Data Scientist with genuine passion for innovation and experience in developing products from concept to mass deployment. If you are resourceful and have a good grasp of statistics, probability and ML/AI and are ready to join a dynamic fast-growing start-up this unique opportunity is for you!Key ResponsibilitiesDesign and implement complex machine learning models and algorithms processing raw radar sensor dataDesign tools for data generation and collection from a single radar source and for fusing data from multiple sensorsManage the machine learning pipeline, including data collection, preprocessing, development, evaluation, and deploymentAnalyze extensive datasets to identify trends and patterns, and preprocess data for enhanced model accuracy and efficiency of people sensing and trackingBuild the foundation for platform-based data collection and analyticsCollaborate with cross-functional teams to integrate data analytics and ML pipeline with the rest of the SW stack on the edgeMinimum RequirementsMSc/PhD in Data Science, Mathematics, Computer Science or a related field4+ years hands on experience in a similar position (Radar / LiDAR / Computer vision)Strong ML research background in conjunction with a strong coding experienceStartup mentality, solution driven, can do approachProficiency in Python and in standard ML tools – TensorFlow / PyTorch / Scikit-LearnProficiency in C/C++Obsessively passionate and inquisitive and seek to solve everyday problems in innovative waysExperience with lightweight / edge ML technologies preferredGood understanding of signal and image processing preferred

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