CPNET · Remote, USA · April 12, 2020
💰 $85,000 - $95,000 / yr
CPNet is an Industrial IoT and AI company founded to bring Industry 4.0 technology to mid-market producers across several traditional manufacturing verticals, helping them to improve productivity of their legacy equipment by providing algorithmic decision-making support. We make it possible to unlock the hidden potential of the existing assets by tapping into and combining their real-time and post-production enterprise data, further running advanced analytics to generate deep operational insights and targeted recommendations.
We are looking for a passionate data engineer to join our team in a hybrid research/engineering role, to both upkeep and advance AI/ML components of our products. The successful candidate will utilize diverse mathematical, optimization, machine-learning, and programming skills to solve challenging real-life problems. She will be presented with multiple opportunities to contribute to product innovation and company strategy, by spotting and introducing best-in-class ML/AI methods and algorithms from research. Strong preference will be given to holders of MS or PhD degrees in Computer Science, Data Science or Industrial Engineering / Operations Research.
Conduct periodic surveys of relevant academic and industry sources to identify novel techniques applicable to the existing and/or new analytics components of the CPNet technology suit, translating cutting edge ML/AI research into robust software implementations
Lead ongoing efforts to enhance performance and functional capabilities of the existing analytics components of the CPNet technology suit, as well as the development of the new ones, in collaboration with and acting upon feedback from the client-facing and/or engineering teams
Participate in development and delivery of the proposals, including demonstration of the analytics components of the CPNet solutions to the customer
Assist with optimization problem formulations, design requisite customizations of the analytics components, maintain engagement with the implementation teams during the development/testing/deployment cycle
Identify, troubleshoot, and resolve production data integrity and performance issues, to support business analysts and/or members of customer success teams responsible for uninterrupted functioning of the deployed analytics components