Location: Brazil
Remote: Yes, or Hybrid or Office
Willing to relocate: Yes, it is possible
Technologies: Data Science, Machine Learning, Deep Learning, Computer Vision. Geospational data and IoT sensors (weather and environmental), satellite images, gridded/array data and time-series.
Résumé/CV: https://www.linkedin.com/in/ilirium/
Email: [kalimulin.jbs] located in the [gmail.com] service
Hi, I have 12+ years in tech and analytics, of which 4+ years are working as a Data Scientist. I completed PhD degree 8 years ago. I have broad experience with machine/deep learning (ML & DL) and data science in computer vision (CV), geospatial & climate data. Solid software engineering skills and mindset. I use satellite images, radar data, and time-series structured data in my projects. Now I am learning Rust and am interested in using it in projects.
Main tech stack: Python and Golang, Numpy, Pandas, SciPy, Scikit-learn, PyTorch, OpenCV, Pillow, Git, Bash, Jupyter, Matplotlib, PyCharm, Docker, GDAL
Key results related to hard skills:
– Create, train and tune DL models
– Statistical processing and data analytics of times series from IoT sensors and satellite images
– Remote sensing and computer vision: georeferencing, warping, image filtering and processing, object recognition, interpolation
– Benchmarking and speed up: async, threads, multiprocessing, multidimensional array algorithms with Numpy
– Developed backends for data analyzing services and prediction API in Python and Golang
– Dashboard for comparing data sources by different statistic metrics
– Deployed services to production and staging with Docker Compose, GitLab CI/CD and Bash
Main tech stack: Python and Golang, Numpy, Pandas, SciPy, Scikit-learn, PyTorch, OpenCV, Pillow, Git, Bash, Jupyter, Matplotlib, PyCharm, Docker, GDAL
Key results related to hard skills:
– Create, train and tune DL models
– Statistical processing and data analytics of times series from IoT sensors and satellite images
– Remote sensing and computer vision: georeferencing, warping, image filtering and processing, object recognition, interpolation
– Benchmarking and speed up: async, threads, multiprocessing, multidimensional array algorithms with Numpy
– Developed backends for data analyzing services and prediction API in Python and Golang
– Dashboard for comparing data sources by different statistic metrics
– Deployed services to production and staging with Docker Compose, GitLab CI/CD and Bash