Adjunct Faculty: Course Developer and Instructor (Remote)

Master’s in Data Science: Governance, Policy and Society (Online program)

View PDF of Position Description

Position Description:

The Data Science Institute at Brown University has an immediate need to hire course developers/instructors (adjunct faculty) for online courses for a new Masters in Data Science: Governance, Policy and Society (these are remote positions).

Qualified candidates should have prior experience in course design, with a preference for experience in online teaching. Course development is supported by Brown University's Digital Learning and Design (DLD) team, which works closely with faculty throughout the development process. Courses follow a 15-week asynchronous format and are delivered via the Canvas learning platform. 

The general time expectations are:

On average, this position requires an average commitment of 5 to 10 hours per week, including a 1.5-hour meeting with the lead DLD course designer. In June, media content creation will take place in Providence, with travel expenses reimbursed.

It is expected that the developer will teach the course in Fall 2025; however, exceptions may be negotiated.

This position offers a stipend of $10,000 to $13,000, based on experience, paid in two installments: half at the start and half upon completion. The same stipend is awarded each time the faculty member teaches the course.

We are looking for qualified candidates who can develop and teach any of the following courses:

Basic AI & Policy Ethics: This course provides an introduction to the ethical and policy considerations surrounding artificial intelligence (AI) in today's society. Students will explore key ethical concerns, such as data privacy, bias, and accountability, as well as the societal and historical contexts that have shaped current AI governance. In addition, the course will offer  a high-level overview of how AI systems are developed, including the basics of data collection, usage, and the training process for machine learning models. By examining these topics, students will gain a foundational understanding of the complex interactions between AI technologies and societal impacts, preparing them for deeper discussions in future courses on AI governance and responsible innovation.

Data Engineering in Disguise: This course introduces students to the core principles of data engineering, emphasizing the often-hidden ethical choices that shape how massive datasets are managed. Students will learn about the fundamentals of data architecture, storage, and processing, while exploring critical values issues such as privacy, biases, data provenance, ownership, and copyright. This course interweaves the  ethical considerations with  the technical mechanics of data engineering, which exemplifies the real-world choices data engineers make as well as  their broader societal implications. By the end of the course, students will understand not just the technical foundations of data engineering, but also the value-laden decisions involved in handling large-scale data.

Evidence-Driven Policy Making: This course explores the role of artificial intelligence (AI) and machine learning (ML) in shaping evidence-driven policy decisions across various sectors. Using case studies from various AI/ML sectors, students will critically examine how AI/ML tools influence policy outcomes. Rather than delving into the technical intricacies of ML, this course emphasizes a "black box" approach, where data inputs lead to predictions. Students will learn to distinguish between prediction and intervention, recognize the limitations of AI/ML, and develop a transparent, precise language for discussing these technologies. By fostering healthy skepticism, this course equips students to make informed decisions about AI's role in evidence-based policy making.

Data Governance

Drawing from the lessons of earlier courses, this course provides a thorough exploration of data governance within the context of artificial intelligence (AI) and machine learning (ML). Students will start by defining AI ethics and core principles guiding AI and ML development, expanding  on key issues such as bias, fairness, and transparency. This course will then explore the current landscape of AI regulation and legislation, examining the roles of governments and international organizations in shaping and enforcing these regulations. Students will discuss the challenges and opportunities associated with AI governance, gaining insights into how regulatory frameworks can both address ethical concerns and foster innovation.

Fairness and Bias

This course investigates the pursuit of building equitable technology by addressing fairness and bias in algorithmic systems. Students will review the latest advancements in creating more equitable algorithms, exploring definitions and types of (un)fairness. The course covers the challenges of explaining machine learning processes, ensuring accountability in algorithmic decisions, and addressing systemic biases. Through a combination of theoretical insights and practical approaches, students will gain a comprehensive understanding of how to design and implement fair and accountable AI systems.

Machine Learning/DL/LLM

This course offers a comprehensive introduction to machine learning (ML), deep learning (DL), and generative AI, preparing students to become informed users of these powerful technologies. The first half of the course focuses on classical ML techniques, while the second half is split between deep learning applications and the emerging field of generative AI, particularly large language models (LLMs). Students will explore key concepts like backpropagation to understand how models are updated with new data, and the differences between pretraining, fine-tuning, and alignment strategies, including Deep Policy Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF). 

Break-down of Job Responsibilities, Supporting Actions & End-Results

1. Major Responsibility:  Develop and facilitate online Data Science course(s) to optimize learning and student engagement

Supporting Actions:

• Develop rigorous, student-centered course content from approved curriculum in accordance with assigned course development schedule and in alignment with online teaching and learning best practices.
• Facilitate an engaging and constructive student learning experience by delivering the course content from approved curriculum in accordance with assigned academic schedule
• Help integrate problem sets into asynchronous and synchronous discussion sessions that provide students with real-world application to data science challenges
• Facilitate weekly, 1.5 hour synchronous sessions for each course
• Manage Canvas Learning Management System (LMS) and associated integrated technology tools (e.g., Harmonize, Ed Discussion, Zoom) including discussion boards, student correspondence, announcements, and day-to-day tasks related to course(s)
• Maintain all administrative/academic components of course(s), including grading of assignments, exams, etc.
• Maintain routine communication with the department faculty, including attendance at applicable faculty meetings

2. Major Responsibility:  Provide consistent academic support to students to optimize academic student success

Supporting Actions:

• Offer regular and  flexible office hours for students to attend and further engage in course material 
• Provide consistent guidance related to course content and answer questions related to course material using virtual platforms including Canvas and Zoom, so that students are up-to-date with academic-related matters
• Identifying creative ways to engage students both synchronously and asynchronously  that further stimulate academic success

3. Major Responsibility:  Serve as a supportive resource for students’ academic concerns to help facilitate a well-rounded and inclusive program experience

Supporting Actions:

• Support students during the administration of course(s) and ensure student engagement, respond to concerns, and direct students to appropriate resources. Keep the Chair and Academic Director informed as needed
• Bring students’ non-academic needs and concerns to appropriate program staff and leadership members
• Collaborate as needed with various faculty, instructors, staff, and leadership members to enhance overall student experience  
• Potentially serve in an advisory role to support students with their final capstone project 

Dimensions

This position will support 1-2 courses, as needed, with multiple sections, and will work closely with Faculty instructors.

Job Qualifications and Competencies

• Masters degree or higher in Data Science, Statistics, Computer Science, or a closely related degree required
• A minimum of 2 years of experience as a Teaching Associate, Adjunct Faculty, Instructor, or Professor in a university setting, or advanced expertise in the field gained through professional experience
• A proficient understanding of course content
• Experience developing and teaching online courses using a Learning Management System such as Canvas is preferred. 
• Experience with adult learners with professional experience is preferred
• Collaborative team player who fosters open communication and cooperation among stakeholders
• Self-disciplined individual who works independently and applies good judgment
• Demonstrated ability to support and promote a community of diverse perspectives and cultures in an inclusive environment.
• Excellent oral, written, interpersonal, and organizational skills
• Background Check: Criminal and Education

Technology Requirements

Adjunct faculty are required to provide their own hardware (PC or Mac computer, and peripherals) and internet access to facilitate the online courses at Brown University. Brown University is not responsible for the purchase, upgrade, or maintenance of the online instructor’s telephone, computer, and internet service. Required software for courses will be provided at no additional cost to the instructor. The following are the recommended specifications for Adjunct Faculty:

• Wired high-speed internet connection (minimum 10Mbps download & upload speeds)
• Up-to-date desktop or laptop computer with minimum 4GB RAM and Intel Core i5 CPU
• Up-to-date operating system: Windows 7 or above, or Mac 10.8 or above (Windows XP/Vista or Mac OS X 10.6 and 10.7 are NOT supported)
• Webcam (laptops with integrated webcams usually work very well)
• Phone (need excellent reception where you plan to teach if using a mobile phone)
• Headset with microphone
• Up-to-date internet platforms, either Chrome or Firefox (Internet Explorer and Safari are not supported)

Application Instructions

Applicants should submit their Curriculum Vitae, cover letter, and the names of three references to Program Specialist, Amanda Whittaker (amanda_whittaker@brown.edu). In the cover letter, we expect applicants to address the reasons for their interest in the position, and which course(s) they are interested in developing and teaching, and why.

All positions require demonstrated ability to work collaboratively with faculty, staff, students, and other Brown and community stakeholders with diverse perspectives and demonstrated ability to contribute to an inclusive environment.