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Data Scientist

Why Work at

Working at is Different.


Team members have the opportunity to work on a variety of companies, industries and topics, rather than remaining focused on a single company and function.  All employees participate in the ideation and vetting processes as we collectively determine what companies to build next.  And if one of the companies we build is particularly interesting to a team member, they always have the option to spin out with that entity.


We are committed to building careers, not jobs.  That means that developers can learn the capital funding process, and data scientists can learn B2B marketing.  We want to help develop well-rounded business leaders – some of whom will use these skills within a technical domain, and others of whom will become company founders and general managers.


Beyond competitive cash compensation, equity participation is across all the companies on which a team member works, effectively creating a portfolio of equity for each team member rather than the binary risk associated with an individual startup.


We look for problem solvers with a history of demonstrated excellence.  But being great at your function isn’t enough.  In a typical week, you might write and test code, conceptualize a pricing strategy, and model AWS cost structures to figure out how to make a SaaS offer profitable.  As a result, our interview process is both tech skill and case-study based.



We are seeking a data scientist to participate as a key team member in envisioning, designing, coding, testing and improving the algorithms that are central to our mission as a company.

Some key challenges will include:

Identifying external datasets and developing API or other methods for accessing them
Fluidly self-educating on existing methods for modeling end-user behavior in a variety of contexts, or developing new methods for doing this when necessary
Designing experiments to answer targeted questions
Teaming with developers to embed algorithms in applications
Understanding business economics, user motivation and other contextual information in order to guide analytical trade-offs, with a focus on “minimum viable algorithm” followed by intensive, iterative improvement



A successful candidate will be comfortable in a fluid, entrepreneurial environment, but one that is focused on developing reusable software applications, not bespoke analytical solutions.

He or she will likely have many of the following characteristics:

  • 2+ years professional experience using statistical software (R, S-Plus, SAS, or similar), relational and NoSQL databases and scripting languages (such as Python).  Ideally, R and Python
  • Familiar with general-purpose machine learning methods, such as neural networks, Bayesian networks, regression, decision trees and so on.  Capable of self-teaching new algorithmic methods easily
  • Passionate about using data to drive strategy and business recommendations.
  • Well-rounded top performer who is able to “crunch the numbers” one minute, and critically think through strategic issues the next
  • Self-starter with a high degree of rigor, organization, and discipline to get things done
  • Able to communicate as effectively in delivering complex data-driven findings with businesspeople, as in discussing machine-learning specifications with engineer



Very strong math, physics, CS or similar degree from a leading program

Extremely high SAT or similar standardized test scores

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