Financial services

Use Cases
  • Ad hoc analysis and experimentation
  • Scheduling runs to collect, aggregate, and cleanse data
  • More productive data scientists
  • Significant annual savings
  • Clear audit trail

Data Science Tool(s): Jupyter, R Studio
Server / Cloud Infrastructure: Amazon Web Services
BI / Analytics Tool: Tableau

Domino helps Coatue deliver better models by accelerating experimentation, increasing productivity for data scientists, and reducing delays when deploying models to production, while reducing operating costs.


Coatue Management, L.L.C. — a technology, media, and telecommunications (TMT) focused investment manager recognizes that smart decisions are more of a science than an art. To that end, they have invested in data science to enhance their investment research process, and deployed Domino to help power their data science team. 1

The Domino Effect

The proliferation of alternative data sources and new computational techniques has created demand to identify signals that can inform investment decisions. Identifying these signals requires rapid testing of ideas and iteration by quantitative researchers.

Domino’s platform, running in Amazon Web Services (AWS), has accelerated the rate of testing ideas and developing models. Leveraging AWS’s scalable compute infrastructure, Domino automates the process of running many experiments in parallel across machines.

When data scientists are happy with their models, Domino handles all the plumbing to deploy them in production.

Iterating on ideas faster speeds up the research process. Being able to more rapidly improve and deploy new strategies makes Domino a valuable tool for us.

Alexander Izydorczyk, Head of Data Science at Coatue


Operational Savings

Domino has helped Coatue increase productivity and achieve significant operational savings.

Data scientists run experiments asynchronously. “You can have multiple R Studio sessions or Jupyter notebooks open, so you test more things in parallel, reducing idle time,” said Izydorczyk.

Executing models is easy, self-service, and scalable. Users can quickly and self-sufficiently spin up environments that meet their needs and preferences; practitioners can seamlessly run their code on powerful AWS compute resources.

Employees are immediately productive; there’s no learning curve beyond existing data science skillsets needed to work in Domino.

Everything is reproducible, validated, and auditable. Because Domino saves every result generated by every string of code, results can be reproduced and validated multiple times, by multiple people. This is important for performance, as well as strong record-keeping and compliance.

Business Drivers

Coatue’s data science team sought tools that would help them improve their quality and pace of work by:

Accelerating the research cycle. “With data science, you always have to try many things. It’s hard to just theorize on what the best answer should be,” explained Izydorczyk. “Most people write a short piece of analysis, run it, write another short piece, run it, write a third short piece, run it, and as a result, the speed of their iteration and development is bottlenecked by that run time. Data scientists need tools that let them run multiple experiments simultaneously.”

Deploying models into production. Before Domino, there was a lag between prototyping a model and putting it into production. “You’re in a perpetual state of having old code running in production. There’s a lot of value in being able to deploy immediately,” said Izydorczyk.


Coatue has deployed Domino in AWS as a central data science platform for both experimenting and deploying models. Domino tracks what models ran, when, and by whom. There is a system of record tracking the generation of new models throughout deployment. This provides clear statistical evidence of custody throughout the model management process.

Coatue has two main technical use cases for Domino:

  1. Developing models using the R programming language. Coatue uses Domino both for the ad hoc exploratory phase of model development, and for more rigorous testing.
  2. Scheduling analyses and models to run on a routine basis.
Why Domino

Beyond the aforementioned platform-based gains, Coatue appreciates the following benefits of working with Domino:

Customer support has been fast and thorough, investing effort to address unique feature requests and challenges.

Employee appeal: Domino allows data scientists to focus entirely on their substantive analysis, using the tools they’re comfortable with, and without having to worry about the underlying infrastructure.

1 Information presented herein regarding Coatue Management is for illustrative purposes only and does not constitute an endorsement by Coatue of, or a recommendation to invest in, Domino.

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