United States of America, Remote

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Who We Are


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 to play next.


What We Are Building

Domino is assembling a world-class Field Data Scientist team dedicated to furthering our mission to unleash the power of data science.  Our Field Data Science team sits at the core of the customer success organization. You would be joining a growing team that is part of a strategic initiative to expand our data science advisory services and partake in the solution engineering council initiatives while having an opportunity to shape the organization's direction. As a core member of our team and the solution engineering council, you will join our experts who possess an overall grasp of the Data Science, MLOps, and distributed computing ecosystem.  

What Your Impact Will Be

We are looking for experienced and motivated Data Science technologists who possess a unique balance of technical depth, understanding of the ML ecosystem, cloud technologies, and strong interpersonal skills. As a senior member of our Field Data Science team, you will partner with customers, Domino Sales, and several other Field teams to create solutions and strategies to address customer business problems and accelerate the adoption of Domino products. You will work with a diverse customer base across various business verticals as a thought leader and representative of Domino. At the same time, you will work closely with many parts of the organization, such as Sales, Solution Architecture, Field Engineering, and Application Engineering, to ensure they can function frictionlessly and achieve their goals. 

The ideal candidate must be self-motivated with a proven track record of building innovative solutions in the analytics and data science space . The ability to connect technology with measurable business value is critical to this role. You should also have a demonstrated ability to think strategically about business, products, and technical challenges and be able to translate customer needs into actionable items for our Domino’s product management and engineering teams to execute on. You will also need a strong understanding of the overall ML and Data Science space, enabling our customers to solve their data science problems by applying statistical and deep learning models on Domino – including data preparation, model building, model deployment, and model management. Additionally, we are also looking for candidates familiar with building large-scale distributed computing solutions in one or more of the following frameworks: Spark,, Dask.


  • Work with customers to design and implement solutions that require integrating Domino into customer-specific workflows, using a mix of native Domino product capabilities, Domino APIs, and related technologies in the analytics ecosystem.
  • Demonstrate ability to think strategically about business, products, and technical challenges and be able to translate customer needs into actionable items for our Domino’s product management and engineering teams to execute on
  • Dedicate up to 50% of your time to the Domino’s Field Solution Engineering Council to either build reusable project accelerators or assist existing Field teams with solutioning work in the broader ML and analytics ecosystem.
  • Build deep relationships with senior technical members within customers to enable them to be both Domino experts and advocates of our products and services to other parts of the company.
  • Analyze current technologies used within the company and develop steps, processes, or solutions to improve customer’s ML Development lifecycle
  • Provide various optimization recommendations to customer’s Data Science project(s) while taking advantage of your expert knowledge of Domino platform features
  • Analyze current technologies used within the customer and develop steps, processes, or solutions to improve their ML Development lifecycle.
  • Offer assistance in statistical data analysis involving data collection and cleaning, data interpretation, data validation, and hypothesis testing via various mathematical modeling approaches, programming languages, and modern technologies
  • Engage with the broader ML engineering and data science communities, by either presenting important reusable solution council work at industry conferences or by speaking at external meetups, or contributing to the Domino blog
  • May require to travel up to 25% of the time (once business travel resumes)

Qualifications :

  • Previous customer-facing consulting experience and 8+ years of solution engineering or data science/ML engineering experience 
  • A degree in computer science, statistics, applied math, physics, or other quantitative subject area, (Masters or PhD) preferred. Will accept a suitable combination of education and experience.
  • Data science or ML Engineering experience required
  • Demonstrated experience in understanding, utilizing, and innovating on state-of-the-art machine learning algorithms and/or statistical modeling
  • Experience developing, troubleshooting and optimizing ML/Data Science models in Python and R as well as package management
  • Experience with machine learning and deep neural network frameworks such as TensorFlow and/or PyTorch
  • Track record of developing data science solution in containerized form using Docker and Kubernetes
  • Proven ability to work with large datasets of terabytes in size utilizing various distributed computing frameworks such as Spark or Dask
  • Experience as a technical leader recognized across teams as a mentor for more junior staff
  • Strong interpersonal, communication, and written skills
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