Working at Domino Data Lab | Glassdoor

Domino Data Lab Overview

San Francisco, CA
51 to 200 employees
2013
Company - Private
Enterprise Software & Network Solutions
Unknown / Non-Applicable
The world is changing. More and more companies are building models into the core of their business. Insurance is priced based on models, cancer drugs are developed using machine learning, and predictive analytics make car manufacturer supply chains more ... Read more

Mission: Make the world run on models.

Company Updates

  • Domino is hosting Rev - a conference for Data Science Leaders. Nate Silver and Cathy O'Neil are headlining. Find out more at:

    http://glassdoor.com/slink.htm?key=vQWkB

    Rev | Data Science Leaders Summit

    Rev will bring together the data science leaders and practitioners of today and tomorrow, May 30-31 in San Francisco. Attendees will learn how to weave data science into the fabric of their organizations as a core capability, making its business impact repeatable at scale.

  • Learn more about Domino through a Q&A with our CEO, Nick Elprin: http://glassdoor.com/slink.htm?key=vQDPt

    Q&A with Domino Data Lab's CEO

    The field of data science is fairly young and evolving extremely rapidly. Finding people who can harness the tornado of big data tech is a major challenge. One of the up and coming vendors who are making data science more accessible is Domino Data Lab.

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Domino Data Lab – Why Work For Us?


One Place for Data Science Work

Develop, deploy, and collaborate — using your existing tools and languages.

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.

Compute Grid & Environment Management enables more data science and less devops

  • Avoid overwhelming your local machine by leveraging scalable compute with powerful, centralized hardware — in the cloud or on premise.
  • Eliminate barriers to leveraging latest deep learning techniques with one-click access to GPU hardware.
  • Reduce software configuration time by running your code in Docker containers, configured to create shared, reusable, revisioned Compute Environments.

Collaboration Hub & Reproducibility Engine

Increase productivity and reduce risk, together

  • Reduce key-man and operational risk by automatically preserving all key project information with Domino’s containerization and dependency map– save data, software configurations, code, parameters, results, discussion, and delivered artifacts as they happen.
  • Streamline knowledgement management with all projects stored, searchable, and forkable.
  • Avoid a cold start by using native integrations with popular source control systems like GitHub.

Data Science Workbench

Faster iteration and experimentation

  • Work instantly by spinning up interactive workspaces with one click — using the tools you already know and love, e.g., Jupyter, RStudio, SAS, and Zeppelin.
  • Tackle complex problems by running, tracking, and comparing batch experiments in parallel with any language, even commercial languages like SAS or Matlab.
  • Minimize changes to your existing workflow by connecting to any data including cloud databases and distributed systems like Hadoop and Spark
  • Instantly leverage all popular tools with the our pre-packaged Domino Analytics Distribution (includes database drivers, Anaconda Python, popular deep learning packages, visualization packages, etc.) Or customize your own environment without risk of affecting other users.

Faster Delivery and manage scalable data products

  • Eliminate delays and risk from recoding of models by deploying models as batch or real-time APIs of your Python and R models.
  • Support web-scale processes with horizontal scalability.
  • Minimize delivery risk with instant rollback to old versions of models.
  • Measure ROI faster: Split traffic across versions to do A/B testing.

Deliver powerful insights to stakeholders

  • Communicate business benefits. Publish visualizations using open source data science tools, including knitr, Plotly, D3, etc. or for commercial tools like Tableau.
  • Expose complex results in business-friendly manner. Publish interactive dashboards and web apps using Shiny and Flask.
  • Remove low-value admin work. Schedule recurring tasks to update reports — serve results through the web or send to stakeholders via email.

Enterprise Identity and Access Management

  • Seamlessly manage platform as integral asset in enterprise IT stack with cost controls and governance.
  • Manage security for consuming and modifying production models.
  • Automate identity management with integrations into LDAP.
  • Manage hand-offs, sensitive information, and regulatory standards with integrated and granular user 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.



Domino Data Lab Reviews

4.9
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Nick Elprin
10 Ratings
  • "Big challenges to solve, and a place you can be proud to work"

    StarStarStarStarStar
    • Work/Life Balance
    • Culture & Values
    • Career Opportunities
    • Comp & Benefits
    • Senior Management
    Current Employee - Sales
    Current Employee - Sales
    Recommends
    Positive Outlook
    Approves of CEO

    I have been working at Domino Data Lab (More than a year)

    Pros

    Overall:
    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.

    Cons

    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.

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Domino Data Lab Photos

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Domino Data Lab photo of: Domino Picnic 2018
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Domino Data Lab Interviews

Experience

Experience
59%
8%
33%

Getting an Interview

Getting an Interview
37%
27%
18%
9
9

Difficulty

2.9
Average

Difficulty

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  1. Helpful (1)  

    Software Engineer Interview

    Anonymous Interview Candidate in San Francisco, CA
    No Offer
    Negative Experience
    Easy Interview

    Application

    I applied in-person. I interviewed at Domino Data Lab (San Francisco, CA) in February 2015.

    Interview

    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.

    Interview Questions

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Domino Data Lab Awards & Accolades

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Pledges & Certifications

Pay Equality Pledge

Committed to paying equitably for equal work & experience

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