Intuit is looking to create AI-powered experiences by developing and deploying machine learning models at scale.
Requirements
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
- Knowledge of machine learning techniques (i.e. classification, regression, and clustering).
- Understand machine learning principles (training, validation, etc.)
- Knowledge of data query and data processing tools (i.e. SQL)
- Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning).
- Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code.
- Experience deploying highly scalable software supporting millions or more users
- Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)
Responsibilities
- Work with AI scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
- Build "machine learning ready" feature pipelines.
- Partner with AI scientists to understand, implement, refine and design machine learning and other algorithms.
- Run regular A/B tests, gather data, and draw conclusions on the impact of your models.
- Monitor and maintain production models.
- Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.
Other
- 7+ years of experience
- Work cross functionally with product managers, AI scientists and product engineers, and communicate results to peers and leaders.
- Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users
- This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs
- Pay offered is based on factors such as job-related knowledge, skills, experience, and work location.
