Discover a smarter way to connect talent with transformative spreadsheet roles.

Welcome to the Monthly Who's Hiring and Who Wants to be Hired?

2 min readMachine Learning

The job market for spreadsheet professionals has been stuck in an outdated format for too long. We believe the template-based approach to hiring and job-seeking in this community is a practical step toward clarity and efficiency, and it deserves wider adoption.

For anyone who has scrolled through a vague job posting that omits salary or location details, the value is immediate. The template forces employers to state what matters: where the role is, what it pays, whether remote work is possible, and what kind of commitment they need. For candidates, it does the same, salary expectations, location preferences, and a link to a resume. This is not revolutionary. It is simple. But simplicity is exactly what a community of experienced professionals needs when time is scarce and precision matters.

What this means in practice is less noise and more signal. When a hiring manager posts "Hiring: Remote, Salary: $120k, $150k, Full Time, looking for someone who can build complex financial models," there is no guesswork. The reader knows immediately whether to engage. Similarly, a candidate who writes "Want to be Hired: Austin, Salary Expectation: $110k, Remote OK, Full Time" signals seriousness and self-awareness. This cuts through the clutter that plagues most job boards and LinkedIn feeds. It respects the reader's attention.

The community reminder is also worth noting: this space is geared toward those with experience. The template reinforces that. It discourages entry-level noise and encourages focused exchanges between people who understand the craft. If you are hiring, use the template. If you are looking, use the template. It is not a restriction, it is a tool. And tools, when used well, make work easier.

From Machine Learning

For Job Postings please use this template

Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for]

Read the original at Machine Learning