Tips from a Recruiter at Pinterest on How to Pivot to a Career in Tech

Ebitie Amughan is a Technical Recruiter for Machine Learning Engineers and Applied Scientists at Pinterest.

Ebitie describes working at Pinterest as the sweet spot between working at a startup and a big organization because you’re not doing everything yourself but you have a team around you and still make an impact. “It's a more mature business, but you also get the opportunity to continue to grow with the company and with the technology.”

Before working at Pinterest, Ebitie was at Microsoft working in business operations. While at Microsoft, she pivoted to tech recruiting so the pivoting tips she shared with us during a Technical Recruiters episode of our Sabio tech series are practical tools and not theoretical.

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How do I pivot?

  • Surround yourself with professionals in the industry that you want to pivot to
    This way, you’ll learn about the lingo and what the role entails. If you don’t know any professionals in the industry that you’re pivoting to, reach out to strangers via networks like LinkedIn and Reddit and ask them about the skills that you need for the role, and how you can acquire them.
  • Identify Transferable Skills
    Explaining transferable skills is you stitching together a story of how skills in your current role or in a previous role can be applied in the new role that you want to pivot to.

Highlight your transferable skills in your resume and practice how you’ll explain it during interviews.

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Include an Objective

Whichever side of this debate you lie on, to include an objective in your resume or not, remember you’re competing against people who have experience in the field that you want to pivot to so writing an objective that will help recruiters understand why you’re pivoting is a plus for you.

An example of this could be that you want to work on projects that impact your community positively which is why you’re pivoting to the role that you’re applying for.

Learn About the Role

Immerse yourself in information related to the ML role from resources like Blogs, YouTube, Webinars, subreddits, podcasts and conferences. Supplementing your knowledge from self-learning with Sabio’s coding bootcamp will give you real-world knowledge for the role and the confidence that you need during interviews.

If you’re in a company that has engineering teams, spend time with them and have conversations about ML. They’ll bring you up to speed on what’s current and you can have discussions about what works and what doesn’t.

Apprenticeship

One way to get mentors is by offering to barter trade your skills. Offer to provide your skills in exchange for mentorship in Machine Learning (ML). This shows a sense of commitment to your prospective mentor and people generally want to help someone that’s putting in effort towards their goals.

Hackathons

Hackathons are a great way to get practical knowledge of machine learning. This is where you can showcase your work or collaborate with other programmers and for some hackathons, you stand a chance to win a job.

Pinterest Labs

According to its homepage, “Pinterest Labs brings together top researchers, scientists and engineers from around the world to tackle the most challenging problems in machine learning and artificial intelligence.”
Pinterest Labs is a lecture series featuring renowned researchers from academia and publications available for free.

Pinterest Engineering Blog

This is a goldmine. There are tons of engineering articles on this blog but you’ll also find specific article on ML, AI, and Data Science.

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PinTalk
This is where you can get Pinterest news and tutorials. It’s geared for the user but a lot of the tutorials like Pinterest optimization, Pinterest business strategy, Pinterest user tracking and more, will show you how this visual search engine functions for the user, insights you need to have when you’re talking about Pinterest during interviews.

Practically Speaking, How do I Explain Transferable Skills on My Resume?

We’ll use Daniel, who attended our podcast session with Ebitie, to paint a practical example. Daniel is a school teacher and a military veteran. This is how he can point out how his experience in his roles as a teacher and Vet can be applied in a machine learning position:

Patience:

The patience that you practice as a teacher is an advantage in coding and programming. Looking for that missing semicolon requires an unordinary amount of patience.

Management skills:

If you can manage a classroom of kids then managing a team should be a breeze.

Discipline:

The discipline that he has from being a vet means that he’ll see projects through to the end and will fulfill the obligations of his role.

Communication skills:

Being a teacher, you have to be able to break down concepts in a way that your students can understand. Being able to explain code also requires good communication skills and it will work to his advantage during interviews.