Data Scientist, experienced

• 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
• Writing code that builds new companies and products

• Comfortable using scripting languages, and relational or NoSQL databases.
• 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 recommendation.
• Well-rounded top performer who is able to “crunch the numbers” one minute, and critically think through strategic issues the next.
• Excited to move fast and know how to prioritize and make critical decisions.
• A self-starter: you have started something on your own before -- an open-source project, a new project within a company, a start-up, or something else.
• Able to communicate as effectively when delivering complex data-driven findings to businesspeople, as when discussing machine-learning specifications with engineers.

• Proven capability in applying machine learning methods to novel problems and driving quantifiable gains in outcome.
• Experience planning and executing work modules that span several months.
• Broad skillset that blurs the lines between data science and software engineering.
• Exceptional computational background (e.g., developed new algorithms and/or has a relevant PhD).
• Exceptional business background (e.g., managing client relationships on technical projects, experience at a top-tier consultancy and/or MBA from a leading program).

• Very strong math, statistics, hard science, CS, economics or similar degree from a leading program. PhD and MBA applicants actively considered 

The Interview Process

Foundry interviews share some common features with other companies hiring Software Engineers and Data Scientists, and have some important differences. These differences are a reflection of the roles our employees play, which are focussed on the early stage development of companies where idea generation, product-market fit and partner interaction may be significant aspects of their jobs. 

All our roles have technical interviews that look for core competencies in the day-to-day tools of Machine Learning, and the ability to discuss technical work, formalize generic problems into a quantitative system, problem solve, and act as part of a team.  Many of our interviewees will recognise these as common topics for Data Scientist and Software Engineering roles, although they may find that we ask somewhat more open-ended questions, care more about collaboration, or draw on a mixed pool of skills. 

We also ask case-study type interview questions, which are less usual for technical roles.  For those who have not heard of these interviews, case studies are open-ended business problems that do not have set answers.  They typically require the interviewee to think through the provided information and the context of the problem, decide what is most important, and then build a structure to answer the most important part of the problem (asking the interviewer for further information where appropriate). 

We ask case studies because Foundry is solving problems that haven't been solved before, and these problems require business-orientated problem-solving and the ability to prioritize in a world of uncertain information and constrained practical actions.  Finding a strategic niche and writing the software that realizes value from it is the heart of how Foundry builds new AI-driven SaaS businesses.

We at Foundry are therefore looking for people who are different, and do not fit into any common mold in technical or business careers (although generalists are frequently star performers in these roles).  Our staff will often describe this as not only wanting to write code to solve a problem but also being able to define the problem that we are solving.