MLOps Engineer - Customer Success

About Comet

Our mission is to help every organization drive business value from AI. Strongly positioned at the forefront of the Gen AI revolution, Comet powers AI developers to accelerate development and execution. From the data scientist tracking training runs to the enterprise team deploying hundreds of models to production to individuals and teams developing generative AI applications, Comet is the platform used by the most innovative builders in the industry. 

Comet is backed by more than $63 million in venture capital funding and powers some of the best machine-learning teams in the world, including Netflix, Uber, Etsy, and Mobileye. We are a remote-first company with offices in New York City (USA) and Tel Aviv (Israel).

About the Position:

At Comet, our mission is to help every organization drive business value from AI. Strongly positioned at the forefront of the Gen AI revolution, Comet powers AI developers to accelerate development and execution. From the data scientist tracking training runs, to the enterprise team deploying hundreds of models to production, to individuals and teams developing generative AI applications, Comet is the platform used by the most innovative builders in the industry. 

As a MLOps Engineer - Customer Success at Comet, you will be responsible for guiding our largest Enterprise customers to accelerate their adoption of the Comet platform, ensuring customers receive the value they anticipated.

The MLOps Engineer - Customer Success proactively works with a broad set of customer stakeholders to illustrate the value delivered through Comet’s software and services. The MLOps Engineer - Customer Success also serves as the internal voice of the customer, advocating on their behalf when working with other Comet teams and partners and driving alignment across Customer Engineering, Deployment Engineering, Sales, Marketing, and Product. You will also work with the broader team to identify and drive key initiatives, processes, tools, and programs to scale for growth.

This position reports directly to the Head of Customer Success. This individual's performance is based on specific metrics associated with customer satisfaction, revenue growth, and retention.

Who you are? 

• You have proven experience as a Customer Success Engineer, Sales Engineer or Solutions Architect
• You are an AI/ML practitioner. You have Hands On experience with AI or ML projects - you have trained your own models, or have been a Data Scientist or ML Engineer

What do you need to know?

• Familiar with the MLOps tooling landscape, ML and AI best practices and experience training or deploying ML models
• Strategic Account Management

Responsibilities:

• Actively serve global accounts including some of the world’s leading organizations in financial services, insurance, pharmaceuticals, transportation, and technology.
• Drive value for customers by helping them achieve their business and technical outcomes.
• Gain an understanding of customer’s use cases and machine learning goals to ensure Comet is strategically aligned to their desired business outcomes.
• Project manage the deployment and roll out of Comet across customer’s data science teams.
• Identify adoption and growth potential in your portfolio and advise your team and customers accordingly.
• Conduct periodic customer health checks to identify risk and work with your team to create and execute plans to reduce this risk. 
• Collaborate cross functionally across teams including Customer Engineering, Sales and Product to ensure customers' success with focus on adoption, value and retention.
• Provide continued input and ideas to scale and improve internal processes and increase operational efficiency for the customer success organization.
• Leverage Customer Health analytics to identify customer upsell opportunities & churn risks.

Requirements:

• 4+ years of proven experience experience with customer success, technical consulting, professional services, or related customer-facing organizations.
• 2+ years of actual proven experience working directly with customers and partners in the data science and machine learning domain, or in the data management space: big data and analytics, data integration, major cloud platforms (AWS, GCP, Azure) is a MUST.