"Temporary Online Course Developer – Time Series Forecasting and Operational Analytics"

Online Course Developer – Time Series Forecasting and Operational Analytics

Location: Remote (U.S.-based only)
Division: Rabb School of Continuing Studies, Brandeis University
Compensation: $3,000.00 (Approx. 65 hours over 12 weeks)

Brandeis University’s Rabb School of Continuing Studies is seeking a skilled online course developer to design and build a new three credit asynchronous online course titled: Time Series Forecasting and Operational Analytics.

This role is for an experienced academic and curriculum strategist to serve as an Online Course Developer within Brandeis Online’s graduate program. The developer will design and build asynchronous, instructor-facilitated online courses aligned with institutional learning outcomes, accreditation standards, and workforce relevance. This course will cover predictive modeling and forecasting under uncertainty, including ARIMA, Prophet, and deep learning approaches for sustainable operations.

Responsibilities:

The development of an online asynchronous course entails the creation and/or selection of elements as outlined in the Brandeis Online Course Standards. Required components include a Brandeis-compliant syllabus, instructor-created materials informed by current industry knowledge, learning objects, and applied assignments and assessments aligned to course and program outcomes.

The Developer is responsible for the substantive content and pedagogical strategies of the course and agrees to uphold Brandeis’s academic standards and online course development guidelines.

Throughout the design process, the Developer will collaborate with Brandeis Online staff, adhere to technical requirements for LMS integration, and meet project milestones. Course drafts will be submitted at designated intervals for feedback, and final approval will be contingent upon a comprehensive design review by a Learning Designer, and Brandeis Online.

Qualifications:

• Advanced degree (Masters or Ph.D) in Statistics, Operational Research, Data Science or a related field.
• Professional experience applying forecasting methods to operational demands, planning, or in sustainability contexts.
• Expertise in time series analysis and forecasting under uncertainty, including ARIMA and modern machine learning approach.