Intuit
Overview
In this role, you’ll be embedded inside a vibrant team of data scientists. You’ll be expected to help conceive, code, and deploy data science models at scale using the latest industry tools. Important skills include data wrangling, feature engineering, developing models, and testing metrics.
Responsibilities
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.
Run regular A/B tests, gather data, perform statistical analysis, and 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.
Qualifications
Model Prototyping: The ML Engineer would be expected to build prototype models alongside data scientists, involving data exploration, high-performance data processing, and machine learning algorithm exploration, and to provide rationale and evaluation metrics.
Model Productionalization: Works with data scientists to productionalize prototype models to the point where they can be used by customers at scale, including automation of training and prediction and orchestration of data for continuous prediction.
Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time, understanding algorithm implementation details.
Machine Learning Tools: Build tools for specific or multiple projects to ease pain points in the data science process, such as speeding up training or improving data management.
BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
Knowledgeable with Data Science tools and frameworks (Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
Basic knowledge of machine learning techniques (classification, regression, clustering).
Compensation Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards, and benefits, in accordance with our applicable plans and programs.
Bay Area, California, CA: $137,500.00 - $186,500.00
Southern California, CA: $124,500.00 - $168,500.00
New York: $136,000.00 - $184,000.00
Pay offered is based on factors such as job-related knowledge, skills, experience, and work location.
To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
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Responsibilities
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.
Run regular A/B tests, gather data, perform statistical analysis, and 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.
Qualifications
Model Prototyping: The ML Engineer would be expected to build prototype models alongside data scientists, involving data exploration, high-performance data processing, and machine learning algorithm exploration, and to provide rationale and evaluation metrics.
Model Productionalization: Works with data scientists to productionalize prototype models to the point where they can be used by customers at scale, including automation of training and prediction and orchestration of data for continuous prediction.
Model Enhancement: Work on existing codebases to either enhance model prediction performance or to reduce training time, understanding algorithm implementation details.
Machine Learning Tools: Build tools for specific or multiple projects to ease pain points in the data science process, such as speeding up training or improving data management.
BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
Knowledgeable with Data Science tools and frameworks (Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
Basic knowledge of machine learning techniques (classification, regression, clustering).
Compensation Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards, and benefits, in accordance with our applicable plans and programs.
Bay Area, California, CA: $137,500.00 - $186,500.00
Southern California, CA: $124,500.00 - $168,500.00
New York: $136,000.00 - $184,000.00
Pay offered is based on factors such as job-related knowledge, skills, experience, and work location.
To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
#J-18808-Ljbffr