Product Manager (AI/ML Solutions)
This is an individual contributor role and the successful candidate serves as the product owner for new AI/ Machine Learning tools and services in support of overall Graph Analytics solutions.
As the Product Manager for AI & Machine Learning Solutions, you are a core member of the TigerGraph Product Management team, helping to define, develop, and deliver state-of-the-art enterprise machine learning tools and services to make it easy for AI practitioners to use Graph in their day-to-day operations. The ideal candidate is a self-starter and can operate in a fast-paced, innovative environment. Successful candidates will have hands-on experience with machine learning frameworks and techniques, such TensorFlow and PyTorch, and be fluent in the infrastructure and processes required to develop and operate machine learning models at scale.
* Champion the cause of how Graph and AI can truly help deliver better business outcomes for customers with industry- and use case-specific reference solutions and starter kits.
* Research and collect requirements to enhance the TigerGraph database platform to integrate with AI/ML frameworks and ecosystem seamlessly.
* Own the roadmap to build and refine Graph Algorithms, ML, and data science libraries to work out of the box as a product deliverable.
* Work with customers, sales, partners, and market input for new and emerging industry use cases and validate implementation.
* Support product marketing to develop product positioning and collateral. Provide technical guidance to customer teams using TigerGraph + ML capabilities.
* 3+ years of technical product management and data science experience, with project experience in machine learning and enterprise software.
* Bachelor of Science in Computer Science, Engineering, Statistics, or similar field. Master of Science or Business Administration degrees preferred.
* Deep understanding of ML pipelines and solution lifecycle. Experience with a major cloud platform (AWS, Google Cloud, Azure) preferred.
* Sound understanding of machine learning techniques, such as logistic regression, naïve Bayesian, SVM, decision trees, and random forests, and their applications to real world use cases.
* Excellent verbal and written communication and presentation skills.