A content creator who covers ways to be creative with AI and ML.
The other day we were reached out to on Twitter about what titles, responsibilities, and skillsets are required for no-coders in the future?
Naturally, this gave us an idea for a blog post. This seems to be a pain point others are experiencing when thinking about how no-code technology will fit into the future of work.
Let’s explore what no-code jobs look like in the future.
This question is interesting because no-code tools are quickly democratizing tech by putting the power in the hands of the no-coders as well as coders. With no-code tech, businesses can create websites, apps, logos, portfolios, landing pages, social media tools, SEO tools, analytic tools, design tools, machine learning tools, and more.
Titles like web developer, front-end designer, or data scientist might mean different things in the future, but the responsibilities still remain. Tasks like building a website or analyzing data will stay in an organization’s workflow, but how they will do it and how they spend their time will adapt to no-code technology.
We predict some titles and responsibilities might look like this:
Obviously AI created a post on User Science for Product Managers a while back explaining how to turn user’s data into a science.
They defined user science like this:
“The study of understanding how the user interacts with your product or company. It starts with data. Capturing the churn rate, purchase data, customer personas, etc., are just some of the aspects of user data that gives you a better understanding of user science."
Another aspect of User Science is creating predictions on what your customer will do if such and such happens or if you modify your product in certain ways.
A part of a Product Manager’s job description is interpreting customer behavior—and this isn’t a new discipline. Each PM should explore the several learning curves to implement innovative tools into their products.
A PM who relies heavily on machine learning to improve or build products might need to shift their focus on user science instead of data science with a no-code ML tool.
Since no-code tools (specifically machine learning) foster collaboration because the barrier to entry is lower, the way we govern no-code tasks will change.
For example, imagine you’re designing something with Canva. A user can add teammates and the team can see a gallery of the collaborative designs they’ve made. The same goes for site builders like WordPress of Squarespace. Many members of the business or a team can design and build without code, giving power to everyone and not just a few individuals.
In machine learning’s case, collaboration and data governance is key to avoid bias blindspots programmers and data scientists may have. We predict a role dedicated to delegating and democratizing data among teams to avoid incidents like the Apple Card fiasco will emerge in the future.
This role sits at the intersection of growth hacker and product manager. A Product Hacker isn’t a new idea as articles about product hacking date back to 2016 promoting no-code.
The article describes a Product Hacker like this:
Product Hacker is a data-driven product manager that uses modern techniques to optimize the value their product provides to its users.
Product Hacker’s responsibilities include feature flagging, feature validation, feature guidance, and product analytics all with a data-driven process.
Concerning Colin’s question about what size companies are hiring no coders, let’s look at where no-code tools fit into workflows and tech stacks.
Being the content guy at Obviously AI, a year-old startup, I use no-code tools every day. Our site runs on Webflow, I design emails in MailChimp, and use our no-code machine learning platform to make predictions and analyze datasets. We’re currently in the growth mindset (and we always will be) so we choose tools that can help us scale.
No-code tools scale.
Therefore they belong at any size company looking to scale their products or business. No-code tools can add many team members to a project, cut down costs of hiring technical coders, and allow agility for startups, SMBs, and large enterprises.
Lastly, there’s a question of salary.
Glassdoor says the average salary of a data scientist is $120,495/year. While the salary is well-deserved because their job is highly technical and difficult, we predict their responsibilities might be altered or split up.
A User Scientists, although hypothetical, are just as specialized as a data scientist and fit into the growth mindset of a company and might appeal to a hiring manager in the future and require a higher salary. Comparatively, a Product Hacker is kind of a no-code-refined version of a Product Manager so their salaries might be similar. A Data Governor fits into more of a managerial high-stress role that will require vast experience governing data teams, and might demand a higher salary.
Generally, we predict we will see more job descriptions with no-code tool experience and how that affects salary is open to debate.
We’d like to thank Colin for asking this great question and putting us in the same category as Zapier, Webflow, and Airtable. We are looking to be a part of the No-Code Community and help discover why no-code ML is so beneficial.
Originally published at Obviously AI. I serve as the Head of Content.
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