At Domino Data Lab, we have an ambitious vision for data science. Our platform helps data science teams accelerate research, increase collaboration, and rapidly deploy predictive models. Our customers are the most sophisticated analytical organizations in the world, including companies like Bristol Myers Squibb, Allstate, Bayer, and Red Hat. Backed by Sequoia Capital, Coatue Management, Bloomberg Beta, and Zetta Venture Partners, we are at the epicenter of the data science revolution, helping companies develop the next breakthrough in medicine, build better cars, or recommend the best song play next.
What we are building
Customer Success Engineers (known internally as "Field Engineers") are an elite team who help data science teams turn their companies into model driven organizations. We train users, advise our customers on how to integrate their use cases on Domino, re-develop team processes, integrate data sources, and customize tools. By joining our team, you’ll work with top-tier data science teams at some of the most advanced companies across industries, including Finance & Insurance, Pharmaceuticals & Life Science, Aerospace & Defense, and more. We have an incredible and diverse team, having a wide range of experiences across industries and academia, that will challenge and enable you to learn new skills that will take your career to the next stage.
As a Customer Success Engineer, one day you might be helping a marketing company starting to work with satellite images or an insurance company get money to their customers faster by integrating NLP into their claims processing. The next day, you might be training a room full of new users or working with an individual data scientist to help them get the most out of Domino. Our Customer Success Engineering team encounters a diversity of technical challenges and opportunities within each Domino customer's account, allowing you to get a firsthand look at what technologies the most innovative organizations are using.
What your impact will be
- Responsible for working with Domino’s most strategic customers to ensure their success
- Build integrations to support custom/advanced data science workflows and how they integrate with the Domino API
- Play an advisory role to our customers to help leverage Domino and data science best practices
- Actively commit to our knowledge base to evolve the way customer success operates at Domino
- Help evolve the way Domino deploys and maintains our software by being in constant communication with core development
What we look for in this role
- Excellent troubleshooting skills
- Strong interpersonal and communication skills
- Willingness to travel up to 10% of the time*
- Deep experience with system architecture, both cloud (AWS, Azure, or GCP) and on-prem environments.
- Programming experience (Python or R preferred)
- Knowledge of data science workflows
- Hands-on DevOps experience in Docker and Kubernetes required
- Understanding of data integrations and data pipelining tools
- Previous working and troubleshooting experience with distributed computing frameworks such as: Spark, Hadoop, Dask, etc.
What we value
- We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply
- We value a growth mindset. High-performing creative individuals who dig into problems and see the opportunities for success
- We believe in individuals who seek truth and speak the truth and can be their whole selves at work
- We value all of you that believe improving is always possible. At Domino, everything is a work in progress – we can do better at everything
- We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company
Based on pay transparency guidelines, a reasonable expectation for the salary range for this role is $152,000-207,000 USD Annually. Information on our competitive total rewards package, including our benefits can be found here. Individual salaries are determined by evaluating a variety of factors including geography, cost of labor, experience, skills, education, and internal equity.