Mission: Make the world run on models.
Domino is hosting Rev - a conference for Data Science Leaders. Nate Silver and Cathy O'Neil are headlining. Find out more at:
Learn more about Domino through a Q&A with our CEO, Nick Elprin: http://glassdoor.com/slink.htm?key=vQDPt
Domino accelerates the development and delivery of models with key capabilities of infrastructure automation, seamless collaboration, and automated reproducibility. This greatly increases the productivity of data scientists and removes bottlenecks in the data science lifecycle.
Faster Delivery and manage scalable data products
Deliver powerful insights to stakeholders
Enterprise Identity and Access Management
Domino is helping companies maximize the value of their quantitative research.
We’re building the platform that enables thousands of data scientists to develop better medicines, grow more productive crops, build better cars, or simply recommend the best song to play next.
I have been working at Domino Data Lab (More than a year)
Leadership places a heavy focus on building a young company "right" --- with longevity and in a way that truly serves our customers. People that work here have generally been at a few other start-ups, and everyone recognizes that there is something "different" about Domino. We have a high bar for what we put out to the market, but also how we do it along the way--- all in the goal of building a lasting software company.
I have not been at another company where post-mortems are standard for things big and small, across all departments (not just for engineering). I'm consistently blown away by how intelligent, earnest and intellectual my coworkers are. People come to Domino because they want to build something big, and that energy and shared sense of mission is infectious. With that said, because of the nature of our business (early to market, category creation) and leadership's commitment to building the company in a principled way, Domino is not for people that just want a high-paying job. The open debate and constant asks for rigorous deconstruction can be tough, and people that are successful here have the "big picture" in mind, as well as grit and adaptability.
Perks: We have all of the standard start-up perks; snacks, catered lunch, fun happy hours and free booze, swag, Pop-A-Shot. Nothing is stunning here, but we're not lacking anything.
Work-Life Balance: People at Domino are expected to put in the time they need to get something done well. I suspect this can vary team to team, but I personally find Domino to be reasonable with demands on my time. Generally, I work 9-5, and a few times a year I'll do put in weekends and nights.
Comp/Benefits: Generally we pay on the higher end of most jobs.
Career Development: From what I've seen, career development means finding a strong mentor or a manager with a commitment to development. Promotion paths aren't structured, but internal promotions happen pretty frequently in an ad-hoc fashion, and it always seems well-deserved.
Management: We have a lot of VPs/executives managing large teams and ICs directly, so there's both an element where Domino feels refreshingly horizontal (you can literally walk up to the CEO or any executive any time, any reason) and that is balanced with a feeling that we could use more middle management to provide support.
Things change very quickly at Domino, and it can often feel like what was really important just yesterday is no longer even a thought today. Constantly having to adjust to new strategies can be taxing, but that is the market we are in. This can be tougher on early career professionals than people that are more seasoned.
Advice to Management
Continue to be honest about our strengths and faults as a company, and make sure to only hire, keep and promote those that are committed to building Domino.
I applied in-person. I interviewed at Domino Data Lab (San Francisco, CA) in February 2015.
There was brief call during which a number of engineering approach questions were asked followed by homework assignments. The interviewer tried very hard to impress with his intellect and vernacular. The homework assignments asked to implement an algorithm in Scala. After submitting an implementation I was asked to change it because it wasn't sufficiently abstract.
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