Demand board · computed August 12
What r/datascience keeps asking for
Recurring demands mined from the community's top posts this week, with the threads as receipts. Not invented: if it's listed, people are actually asking.
Practical guidance, tooling and governance for using AI/LLMs in data-science workflows (in
People are asking for concrete best practices and tools to integrate LLMs safely and productively — including when to let AI write code vs. keep coding skills, how to connect AI to company data, and how to detect/verifyL
Design patterns and architecture for large-scale forecasting systems
Requests for end-to-end recipes: how to design, evaluate, and operationalize forecasts across many SKUs/regions and move an MVP into production at scale.
How to apply embeddings and foundation models in domain-specific work (e.g., geospatial)
People want practical examples, open-source tools, and workflows for using embeddings and foundation models in specialized domains like Earth observation.
Practical strategies and templates for eliciting information from colleagues/stakeholders
Requests for communication techniques, question templates, and processes to get the data/requirements needed in hybrid or remote teams.
Learning paths, transition help, and feedback for communicating technical topics
Newcomers and career-changers are seeking curated study resources, mentorship/transition advice, and peer feedback on explanations/tutorials (e.g., teaching MCMC).
Get the board when it changes
Thinking of engaging here? Check r/datascience's self-promotion rules first, and the wider method in the Reddit playbook. Thread Otter runs this same mining continuously on YOUR buyers' communities, with replies drafted when someone asks for what you build: start free.