Zebra Technologies is a community of innovators creating smart solutions for their customers. They are seeking an Artificial Intelligence Scientist II to develop AI products optimized for speed, reliability, and scale, while collaborating with cross-functional teams to enhance their AI product suite.
Supports the development of new ML techniques and algorithms through data analysis and proofof-concepts
Develops production level machine learning software using PySpark and Python
Converts proof-of-concepts and feature requests into production ready code
Optimizes and transform existing software for speed, reliability, and scale
Integrates state-of-the-art machine learning algorithms as well as the development of new methods
Collaborates cross-functionally with data scientists, data engineers, product managers, and other stakeholders to identify gaps and issues in the AI product suite and propose solutions
Participates in all phases of the software development lifecycle including design, coding, unit testing, and documentation for both new and existing pieces of software
Drives innovation by fostering open, high energy, collaborative environment; lead participation in innovation summit and expos, recommend relevant training and conferences for employees to attend, publish paper and patent disclosures
Qualification
Required
Bachelor's, Master's, or Ph.D in computer science, computer engineering or related field, mathematics, statistics
Minimum 2 years' experience in data science, machine learning, or software engineering required
Preferred
Ability to be agile and thrive in a fast paced environment
Ability to work independently and take initiative, but also a co-operative team player
Highly skilled problem solver
Has the ability and enjoys independently research complex problems
Knowledge of programming techniques and languages (e.g., Python, PySpark)
Working knowledge of common machine learning and deep learning approaches (e.g. regression, clustering, classification, dimensionality reduction, supervised and unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning) and data analysis packages (e.g. scikit-learn, Spark MLlib)
