Posted about 1 year ago
DataRobot is looking for Software Engineers with Data Science experience who are passionate about Geospatial to build cutting-edge location-based technologies.
As a Data Science Engineer on our Geospatial team, you will work on our machine learning platform and actively contribute to the development of our state-of-the-art preprocessing and modeling capabilities.
The Geospatial team owns the entire data flow from ingestion, modeling to data visualization, working closely with our Core Modeling team to ensure our models and modeling automation are the best in the world.
We are looking for talented people with excellent engineering skills and deep knowledge of Machine Learning who can analyze problems, develop innovative solutions, and implement them for real-world use on top of our platform.
DataRobot is based around delivering best-in-class data science solutions and this position provides the opportunity to build the key data science components of our system. Responsibilities
- Automate machine learning processes
- Design and build machine learning models for accuracy and scalability
- Integrate machine learning algorithms with other applications and services
- Recommended background: 5+ years of combined Python engineering and machine learning experience
- Experience writing maintainable, testable, production-grade Python code
- Understanding of different machine learning algorithm families and their tradeoffs (linear, tree-based, kernel-based, neural networks, unsupervised algorithms, etc.)
- Good command of scientific Python toolkit (numpy, scipy, pandas, scikit-learn)
- Geospatial experience using libraries and technologies such as GeoPandas, GDAL, PostGIS, PySAL, etc.
- Understanding of time, RAM, and I/O scalability aspects of data science applications (e.g. CPU and GPU acceleration, operations on sparse arrays, model serialization and caching)
- Software design and peer code review skills
- Experience with automated testing and test-driven development in Python
- Experience with Git + GitHub
- Comfortable with Linux-based operating systems
- Experience with large-scale machine learning (100GB+ datasets)
- Experience with deep learning libraries and frameworks (TensorFlow, Keras, PyTorch etc.)
- Competitive machine learning experience (e.g. Kaggle)
- Previous experience of deploying and maintaining machine learning models in production