Write for Deepnote

Remote

Deepnote is a next-generation data notebook — a beautiful new type of computational medium that helps data teams solve the most challenging problems they face today.

As part of our mission, we’re sponsoring writers and content creators to publish high-quality posts that help data scientists and data analysts.

We are currently looking for contributing writers to help us create high-quality guides on exploratory analysis, data science, and collaboration within data teams.

✍️ Why write for Deepnote?

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Write content that you are interested in.

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Write under your own name and build your own following. Develop your personal brand in the data science community.

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Get exposed to a large and engaged community of data professionals.

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You’re free to republish the content anywhere you like.

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No deadlines, write at your own pace, as much as you want or as little as you want.

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We’ll compensate you well (and send some cool Deepnote swag your way)!

📖 How does it work?

1. Fill out the application form. You’ll need to tell us a little about yourself and include links to 2-3 of your previous articles that demonstrate your expertise and writing ability. If you don’t have existing articles, please submit an article you plan to publish.

2. We will evaluate your application within a week and get back to you. If we think it could be a fit, we will schedule an intro call and chat about how we can work together.

3. Start writing and publishing!

4. 14 days after your article is published, you send us a screenshot with stats about the reach so that we can calculate your reward accordingly.

5. You send us an invoice for the agreed amount and get paid!

📌 Topics of interest

We are particularly interested in the following topics:

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Articles about notebooks (data science notebooks, Python notebooks, SQL notebooks, Jupyter), data platforms and data workspaces.

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Articles on collaboration and workflow in data science teams.

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In-depth reviews and comparisons of data analysis tools.

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Deep-dives into modern data science tools and libraries (Python, SQL, visualizations, BI, Snowflake, Big Query, Redshift, …).

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Tutorials that teach complex data science concepts in an approachable way.

You might have noticed that we generally prefer topics that are relevant in the business context.

The format is fairly open — from articles to YouTube videos to LinkedIn posts and more.

Each article needs to mention and link to Deepnote. For example, if you are writing a tutorial on collaboration, you could link to one of our blogposts on collaboration. If you are writing about Snowflake, you could link to our integration with Snowflake. If you are writing a tutorial on exploratory analysis, you can link to a notebook the readers can use as a template.

💰 Compensation

We try to match the compensation as closely as possible to the value it brings to data folks. We sponsor high quality content, but if people don’t see it they can’t get value from it. As a result, the compensation depends on the amount of traction your content receives:

How do we classify reach?

How do we classify publications?

Content that no one sees is not valuable content. We look at the reach in the first 14 days after publishing to encourage writers to distribute their content to data professionals.

There is no easy way to do this and each publication is pretty unique (especially non-traditional content formats), but below is a rough guideline on to think about it.

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High reach: If the content is published on Medium then 10k+ views. If Twitter or LinkedIn then 100k+ views.

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Tier 1: These are the publications with high reputation (Wikipedia, Nature.com) or publications that are highly esteemed in data communities (towardsdatascience.com) with a domain authority over 80.

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Medium reach: If the content is published on Medium then 1k+ views.

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Tier 2: Not yet generally recognized as well-known publications, but still relevant for data scientists and data analysts. This could be more niche publications, blogs of data companies, youtube channels, newsletters, or personal blogs. The publications should have a domain authority over 60.

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Low reach: It’s sad to see high quality content go unnoticed. This means any reach that doesn’t qualify for High or Medium reach. Please note we don’t compensate content that didn’t get meaningful enough traction and didn’t reach data scientists and analyst who’d need it. The only exception is if the content gets published in Tier 1 publication which means a considerable amount of effort went into the article and just simply might be too niche for the wider audience.

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Tier 3: Social media such as Twitter or LinkedIn. Sometimes you have something short but valuable to share that wouldn’t work well as a separate article. If your post goes viral and mentions Deepnote (url or @DeepnoteHQ) it’ll qualify for compensation.

ℹ️ A few rules for your article to qualify

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The article needs to stay up for at least 6 months. (You’ll get paid earlier than that, usually after 14 days of publishing but if we notice articles disappearing after we might not continue the collaboration.)

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The article needs to mention Deepnote and link to deepnote.com (any subpage including documentation and projects) with a do-follow link (whenever possible).

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If the article is published on Medium and requires an account to view story, the link to Deepnote needs to be included in the first few paragraphs that are shown to non-members too.

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It’s fairly easy to generate fake/bot traffic to your content. This doesn’t help the data community and we are not compensating such content as a result.

💡 Tips

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Submit your article to relevant communities on Reddit, Hacker News or talk about it on social media. This will get you extra distribution relatively quickly.

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If you writing about a specific tool, you can reach out to the authors of those tools on Twitter or LinkedIn. They will often be happy to retweet or repost your article.

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Write for major publications! They are often looking for talented authors and have a compensation scheme on their own. You’ll get paid twice. 🤑

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Ask to write guest posts for startups focused on data teams. They are often looking for new content.

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Twitter and LinkedIn posts are a great place to test out ideas. One retweet/repost from an influencer could make your post go viral.

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You can turn your article into a video and vice versa. Then you can submit both pieces of content.

✉️ Thanks for reading and looking forward to hearing from you!