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Intern Data Scientist, Geo Maps

External
Grab logoGrab · Cluj Napoca, Romania
ContractOn-site1w ago
ClassificationClusteringComputer VisionDeep LearningForecastingMachine Learning
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Vision insurance

Additional Information

Get to Know the Team The Data Science (Geo Vision) team focuses on improving maps and building map-based localization, routing, travel time estimation, and traffic forecasting. These efforts support multiple Grab services. We use a range of techniques, including Computer Vision, NLP, Information Retrieval, Text Mining, and conventional machine learning. These techniques allow us to process a variety of signals, such as images, videos, text, sensor readings, and GPS probes. We use these signals to understand locations and road networks. Get to know the role We are looking for an intern data scientist to help automate the process of map creation using data science techniques. We believe you have at least basic modern vision skills and deep learning knowledge. You will be on site and be reporting to the Senior Engineering Manager based in the Cluj Napoca Office. The Critical Tasks You Will Perform You will develop architectures to address the latest demands of computer vision and machine learning algorithms. You will collaborate across DS teams 3 months internship, 8 hours/day, starting July/August What Essential Skills You Need Familiarity with Python and some of the following libraries: PyTorch, Tensorflow or Fast.ai; NumPy; OpenCV; scikit-learn; Understanding of machine learning methods for classification, regression, and clustering. Learner who keeps updated with the current state of computer vision and deep learning techniques/architectures. Proficient in English What We Stand For at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.


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