Today we’re announcing that we have raised $10.5 million in a funding round led by Sequoia Capital.
For us, fundraising is simply a means to an end: building the leading data science platform to help companies maximize the impact of their quantitative research. That said, moments like this are a good time for reflection and I wanted to share three thoughts with you.
First, we raised this money right now because we see the market for Domino exploding and we want to throw gas on the fire. Our customers are some of the most sophisticated and analytical organizations in the world. They range from leading pharmaceutical and life sciences companies, to insurance companies, to financial services firms that run the gamut from banks to ratings agencies to hedge funds. Innovative consumer technology companies like Instacart and Mashable use Domino, as well.
This customer demand has underscored for us how much opportunity Domino has to expand, both by improving our product and growing our customer base. Today’s funding round is all about providing the money to do both.
Second, we are thrilled to have Sequoia lead this round and join a group of stellar investors who previously backed Domino, which includes Zetta Venture Partners, Bloomberg Beta, and In-Q-Tel. What attracted us to Sequoia – and the investors who led our previous round – is their emphasis on building a world-class company. Every discussion with Sequoia begins with the question, “What would a world-class company do?” That’s precisely our goal and it’s great to have partners who are not only aligned with that goal, but amplify it.
Finally, the saying goes that timing is everything in startups — and we are seeing the world moving quickly in the direction Domino envisions. More companies are investing in quantitative research and data science as a core organizational capability. As this work moves closer to the heart of the business, it’s being done more collaboratively. Companies are more concerned than ever about compounding their knowledge, and especially in regulated industries, there’s an increasing awareness that data science work needs to be auditable and reproducible. This is the exact world Domino envisions, which is both gratifying and thrilling.
When Chris, Matthew and I started Domino over three years ago, we were coming out of Bridgewater Associates, one of the world’s largest hedge funds. Because quantitative research was the heart of the business, Bridgewater was probably 10-20 years ahead of much of the world in implementing a mature, disciplined, scalable process for developing models and algorithms.
As we saw more organizations investing in “data science” capabilities (whether it be actuarial research, quant research, biostatistics, etc), we saw a set of problems that highlighted a missing part of the analytical stack: data science and quant research teams needed a “place to work.” This is analogous to what CRM is for sales teams, or what version control is for software engineering teams.
Domino fills that gap by providing a system of record and a collaboration hub for teams of data scientists and quantitative researchers. Our customers find this valuable for three reasons: it makes researchers much more productive, it makes it easier to productionize data science output, and perhaps most exciting to us, it facilitates collaboration, reproducibility, and reusability of research, so companies advance research faster by building upon past work.
With this investment, we will accelerate our product development, helping our current and future customers build better cars, develop more effective medicine, increase crop yields, give people better rates on insurance policies, or make a breakthrough wherever data science finds new fertile ground.
We can’t wait to show more of the world what we’re building. And if our mission resonates with you, come join us, we’re hiring!
New to Domino? Consider a Guided Tour.Start Your Free Domino Trial
Recent PostsPolars - A lightning fast DataFrames library Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration Tensorflow, PyTorch or Keras for Deep Learning Reinforcement Learning: The K-armed bandit problem KNN with Examples in Python Computer Vision in Deep Learning: An Introductory Guide Supervised vs. Unsupervised Learning: What’s the Difference? Powering Up Machine Learning with GPUs A Guide to Natural Language Processing for Text and Speech What Is Reinforcement Learning and How Is It Used?
Other posts you might be interested in
Subscribe to the Data Science Blog
Receive data science tips and tutorials from leading Data Scientists right to your inbox.