Data Scientist — Experienced (Actifai)
Washington, DC /
Actifai – Data Science /
Actifai (a Foundry portfolio company) is seeking experienced data scientists.
Please note that we have a separate hiring process for candidates directly from college, and will not be accepting undergraduate submissions to this posting.
Actifai is an artificial intelligence company. We help clients – primarily those in the cable and telecom industry – optimize their high value, high leverage decisions. Typically this includes things like customer acquisition, customer retention, and customer development (upsells/cross-sells).
Actifai was created in the Summer of 2020 in partnership with a major cable operator.
Actifai is part of Foundry.ai, a technology fund/studio that creates AI software companies in partnership with large global enterprises. Foundry’s operating companies focus relentlessly on ‘practical’ applications of AI that cut through the hype cycle and drive immediate, measurable, and recurring improvements to financial performance. Foundry is backed by approximately $100MM in capital from leading private equity and venture capital partners.
The Data Scientist will participate as a key team member in envisioning, designing, coding, testing and improving the algorithms that are central to our mission as a company. They will work in continual collaboration with software engineers and partner company stakeholders.
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; writing code that builds new companies and products.
The Successful Candidate
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.
The programming languages and tools used most frequently at Actifai are: Python, SQL, Node.JS, R, Github, AWS, Docker and shell scripts. We do not expect candidates to be experts in all of these, although a strong proficiency in Python and the ability to learn new languages as needed are common.
Entry level candidates will likely have many of the following characteristics:
- Comfortable using scripting languages, and relational or NoSQL databases.
- Familiar with general-purpose machine learning methods, such as regression, decision trees, neural networks, Bayesian networks, 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 or university, 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.
Senior candidates will often differentiate themselves with some of the following:
- 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).
Finally, we highlight that excellence has no single mold, particularly in a field as rapidly evolving as AI when looking for candidates that mix business intuition with coding skills. We welcome applications from candidates with diverse backgrounds.
Very strong math, statistics, hard science, CS, economics or similar degree from a leading program. PhD and MBA applicants actively considered.
The Interview Process
Actifai 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 recognize 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 Actifai 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.
Our staff will often describe this unique mindset as not only wanting to write code to solve a problem, but also being able to define the problem that we are solving.