Education Coordinator

Posit (formerly RStudio) creates great software that helps people understand data and make better decisions in real-world applications. Our core offering is an open source data science platform, and we aim to make it available to everyone, regardless of their economic means. We’re a Certified B Corporation®, which means that our open-source mission is codified into our corporate charter.

About the Role

We’re developing a learning product named Posit Academy to support Posit’s mission of making data science accessible to all. We believe that doing good data science, like playing the piano, is a skill that cannot be developed with traditional workshops and online courses alone. We’re helping learners develop their skills with a mentor-led, computer supported learning environment. This role will oversee the design, development, and delivery of educational content in this environment.

We’ve had great success with early customers but we’re still rapidly iterating and experimenting in order to fully realize the product’s potential. Additionally, we have ambitious plans for scaling this product from hundreds of users to thousands over the next year and you’ll be an integral part of making that happen. This team has all the excitement of a startup, but with the stability and work-life balance that comes from working at a profitable, mission-driven company.

Things you will own

As an Education Coordinator, you will be responsible for helping employed professionals learn to do data science. You will:

• Mentor small groups of adult learners as they complete apprenticeships with R or Python. Each apprenticeship lasts about 12 weeks and guides a group of 6-8 learners through the tutorials, exercises, and projects. As a mentor, you will
• Facilitate weekly group sessions where students will share their data science project milestones
• Meet with students online each week, in individual or group sessions
• Engage with students over Slack or MS Teams channels
• Create a positive, fun, and judgment free learning environment and encourage active participation. Where appropriate, you will provide students with useful feedback and tips on how to improve their R or Python programming
• Set-up projects for customers following Posit templates and course syllabi
• QA customer projects
• Triage customer feedback on projects
• Serve as a point of contact with customers during their apprenticeship. You will:
• Help customers optimize their investment in Academy
• Answer questions and resolve issues as they arise
• Document customer progress and feedback for internal tracking
• Report out to customers regarding student progress

Things you will help with

You will collaborate frequently with Engineers, Designers, and other Educators. You will:

• Support staff mentors by leading regular check-in meetings
• Experiment with other mentors to optimize the apprenticeship format
• Automate important tasks
• Identify process improvement opportunities
• Provide product vision for software features, sharing pedagogical principles with your teammates

Things you will bring to the team

Ideal candidates will:

• Enjoy helping others reach their goals
• Be eager to speak and code in front of students, but prefer to nudge students to try it themselves
• Know R well including the tidyverse
• Know Python well including pandas and scikit- learn, and have two or more years of experience using Python to do data science tasks
• Have experience collaborating with others via git and github.com
• Enjoy working in a startup-like environment with lots of autonomy and a strong sense of mission
• Have a strong sense of empathy. You know how to explain technical concepts in a way that others will understand, you know how to make lessons enjoyable, and you can deliver feedback without demoralizing students

Things we will help you learn

It would be a bonus if you knew about these things as well, but we are happy to teach you:

• Modern models of how the brain learns, such as Cognitive Load Theory, Deep processing, Multimedia Learning Theory, mastery learning, spaced (or interleaved) practice, and active learning
• Enterprise data science, including the basics of: publishing data science artifacts, working in a server environment, selecting architecture for data scientists, customizing deployment methods, connecting to and working with databases, managing packages, and more
• Further data science topics such as plotnine, siuba, Shiny, Tidymodels and emerging technologies