Intuit is looking for an ML Engineer to help conceive, code, and deploy data science models at scale using the latest industry tools.
Requirements
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
- Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).
Responsibilities
- Model Prototyping: The ML Engineer would be expected to build prototype models alongside data scientists. This may involve data exploration, high-performance data processing, and machine learning algorithm exploration.
- Model Productionalization: Works with data scientists to productionalize prototype models to the point where it can be used by customers at scale.
- Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time.
- Machine Learning Tools: The ML Engineer would build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one project.
- Discover data sources, get access to them, import them, clean them up, and make them “machine learning ready”.
- Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
- Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
Other
- BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
- Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models.
- Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
- Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.
