Pixxel is a space data company building the health monitor for our planet. We are doing that by building a constellation of some of the world’s most advanced earth imaging satellites and the analytics platform that will enable easy application of that data across a multitude of sectors. The fully deployed constellation will be capable of beaming down high quality imagery of every place on earth every day. This role will contribute towards developing, training and delivering deep learning models and tools to extract various features and value added services using multi-dimensional satellite imagery.
If you love working at the cutting edge of technology and want to contribute towards making the world a better place by bringing down the benefits of space down to earth, this is the place for you.
Details on the role:
- Research and develop scalable analytical models based on Statistical Techniques and latest Approaches
- Build usecases and Create innovative insights from the Optical and Microwave imagery data to solve the real world problems.
- You will also be responsible for building prototypes in order to validate the company's business use cases.
2 - 4 years
- Prior experience in the Agriculture Technology (Ag-tech) domain is a must with 2+ years of relevant work experience in developing crop advisory products.
- Hands-on experience in working with Python, GIS tools, with a taste for writing maintainable, well-documented and high-quality code.
- Knowledge in developing statistical based models for farm based solutions and building scalable products.
- Familiarity in handling and developing usecases using Synthetic Aperture Radar (Sentinel-1) and Multispectral (Sentinel-2/Landsat) data.
- Sound understanding of deep learning, image processing and remote sensing concepts.
- Must have a strong proficiency in programming languages preferably Python.
- Experience in handling satellite imagery and other geospatial datasets.
- Should have good problem solving and analytical skills.
- Good communication and presentation skills.
- Experience in managing and building data science pipelines.
- Sound knowledge of best software engineering practices for all stages of software development life-cycle, including coding standards, code reviews, testing, deployment.
- Hands-on experience working with large datasets.
- Experience publishing papers in reputed journals/conferences.