As the world moves to a mobile-first economy, businesses need to modernize how they acquire, engage with and enable consumers. Prove’s phone-centric identity tokenization and passive cryptographic authentication solutions reduce friction, enhance security and privacy across all digital channels, and accelerate revenues while reducing operating expenses and fraud losses. Over 1,000 enterprise customers use Prove’s platform to process 20 billion customer requests annually across industries, including banking, lending, healthcare, gaming, crypto, e-commerce, marketplaces, and payments. For the latest updates from Prove, follow us on LinkedIn.
Prove is driving the future of digital identity. We are looking for Provers who know how to make an impact. We’re talking self-starting professionals who thrive in a fast-paced environment, process information quickly, and make intelligent decisions. The work is challenging and requires not only smart but natural curiosity and tenacity. Teamwork is also important to us – we work together and play together.
Prove has big plans, and we’re excited about the future. If this sounds like the place for you – come join our team!
Data Scientists at Prove work on Proof of Concepts (POCs) with Sales, Customer Success (CS) related projects with Customer Support Team and Research & Development (R&D) with Product and Engineering. This position of MLOps Engineer can be viewed as a person responsible for expanding and optimizing our data and machine learning pipeline architecture. Full-scale data science products need to be deployed and this role demands a person who has experience with software/DevOps/Data Engineering.
Primarily this position involves using engineering skills to build a production grade self-service analytics framework that provides Data Science more autonomy and control. Data Scientists can then use the framework for all prototyping work or to deploy models into production in swift fashion with a low overall cycle time. This position requires the ability to create a platform that can score a single transaction in real time, while working within production constraints & considerations.
The incumbent develops best practices, policies, procedures and governance of ML models in production, model lifecycle management, and associated data engineering processes designed to deploy ML and AI models efficiently and reliably in production in cloud-based and on-premises environments. Other responsibilities include increasing the maturity of existing ML pipelines by standardizing the data science development lifecycle, identifying and enforcing standards for model experimentation, model validation and testing. Duties will include evaluation of new ML cloud tools and data science methods available on AWS/GCP, and their selection and introduction to the wider organization, education and onboarding of Data Science teams to new technologies. Finally this position will help with any automation needed to refine the Data Science analytics pipeline.
Key Responsibilities:
This position description should not be considered the final description of the position. The position description is not intended to be an all-inclusive list of duties and standards of the positions. It should be assumed that we would, to some extent, structure responsibilities in accordance with the successful candidate’s capabilities and changing business conditions. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor.
The salary range for this role is $158,000 - $210,000 company bonus and stock options. Offered salary will be determined by the applicant’s education, experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data.
Benefits & Perks for FTE Provers:
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