Data Scientist — All Experience Levels

Actif.ai•Washington, DC

67d•Hybrid

About The Position

Actifai is seeking experienced data scientists. 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. The Company 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 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 on practical applications of AI that 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 Position 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. __ 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). __ Academic Qualifications Candidates should hold a very strong CS, math, science or similar degree from a leading program. PhD applicants are actively considered. Successful candidates 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, Javascript, Github, AWS, Google Cloud, Docker, Kubernetes, 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.

Requirements

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.
Candidates should hold a very strong CS, math, science or similar degree from a leading program.
strong proficiency in Python and the ability to learn new languages as needed are common.

Nice To Haves

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).

Responsibilities

envisioning, designing, coding, testing, and improving the algorithms that are central to our mission as a company
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