How to Land a Job in Machine Learning or Data Science

Insights From Expert Technical Recruiters for AI Jobseekers

AI can seem like a dream profession: Machine learning engineers and data scientists are in-demand and well paid. On top of that, many find the day-to-day work of building models, tuning algorithms, and analyzing data to be deeply satisfying. But the field is also competitive and fast-paced. Job seekers, especially those early in their careers, are often confused about the best way to get hired.

Have you struggled with:

Writing a resume?
Preparing for interviews?
Finding job listings that match your skills and interests?
Understanding why recruiters or hiring managers don’t respond?

In December 2022, DeepLearning.AI gathered a group of experienced technical recruiters and asked them some of the biggest questions about landing a job in machine learning or data science.

Our guests:

Nikita Gupta is a senior technical recruiter at Uber. Previously she worked at Amazon and a number of startups.

Jeff Bank is a veteran talent attractor who has helped build engineering teams at Google, Microsoft, LinkedIn, Roblox, and more.

Puneet Kohli co-founded (with Nikita) CareerFlow.ai, which offers a free LinkedIn Optimization tool for job seekers. He previously built computer vision systems for Apple, Amazon, and other tech companies.

Linda Lee is a partner at the venture studio AI Fund, where she specializes in talent acquisition. She is also a board member of Factored AI, a startup that helps companies find skilled machine learning and data science workers.

What are the most common AI roles in today’s job market?

Puneet said that most AI roles fall into one of three major categories. He described them as follows:

Machine Learning Engineers: These software engineers are responsible for building and managing data pipelines as well as the infrastructure needed to train and deploy AI models.
Machine Learning Practitioners: In this role, your goal is to build and/or improve the machine learning model itself. Responsibilities include developing model architectures, training models, adding data sources, and tuning parameters.
Data Scientists: These professionals use statistical and machine learning techniques to extract insights from data. They may also be involved in collecting and cleaning data, as well as visualizing and communicating the results of their analyses.

How to Write Resumes That Stand Out To AI Recruiters/Hiring Managers

Technical recruiters see, on average, about 200 resumes for each job listing, Nikita said. “Why should I select you? If your resume cannot answer that question, then you need to go back and make some changes,” she said.

How can you stand out? “There is no single golden point that applies to everyone,” said Puneet. “It comes down to what each person brings as an individual and whether I can see that clearly from their resume.”

Here are five ways to make your resume show your worth as a candidate.

Do your homework: You can show the recruiter that you pay attention to detail by tailoring your application to the role described in the job posting. “Don’t use the same resume and cover letter for every role,” said Nikita. Read the description carefully, paying extra attention to the responsibilities and qualifications. Then, go through your resume and emphasize any skills, experiences, and projects that match the company’s needs.
Substance matters: Don’t just list your skills. Describe in detail how you have used each of them, Jeff advised. This lets recruiters know you are an experienced practitioner and not just someone listing buzzwords to grab attention. If you don’t feel comfortable talking about a subject during a technical interview, it’s best to leave it off your resume.
Show your impact: Recruiters and hiring managers love to see results. Don’t just say you developed a recommendation model; say you developed a recommendation model that increased visitor rates by 20 percent, said Nikita. Data lets recruiters and hiring managers visualize your impact.
Provide links: Recruiters may want to check out all of your projects and past work. Nikita said she appreciates when candidates make it easy by adding clickable links to blog posts, Github repositories, or websites associated with projects listed on their resumes.
Do personal projects: “I look for people who are contributing to open-source projects, have side hustles, or are working on apps,” Jeff said. This demonstrates initiative and shows you are staying on top of advances in the field beyond the requirements for your core work responsibilities.

How to Know if You Are Qualified for an AI Job Posting

Have you ever felt confused by an AI job listing because it listed more skills than anyone could possibly have?

Don’t panic. No candidate is a perfect match for any role. “Go ahead and apply even if you are only a 60 or 70 percent match,” Nikita said. You can also fine-tune your application by asking the recruiter which skills are must-haves. Focus on those.

How to Reach Out Directly to Technical Recruiters

When applying for a job, it pays to be proactive. Connect directly with the recruiter ahead of time to ensure they notice your resume. Some job applications will list the recruiter’s contact information. Or, look for recruiters who work for companies that interest you by searching LinkedIn.

Here are some pointers for presenting your best self when you reach out:

Be clear. “Make sure your message is clear,” Nikita said. She suggests briefly listing the top three things that make you stand out along with a call to action. And don’t forget to attach your resume!
Be personal. “It’s a turnoff when a candidate reaches out to everyone with the same canned message,” Linda said. You shouldn’t copy and paste your cover letter from one job to the next, and you shouldn’t do so with emails or LinkedIn messages either. Address each person you reach out to with a customized message.
Be friendly. “Add a line or two sharing anything you have in common with the recruiter, the team, or the company,” says Puneet. For instance, did you go to the same college as the recruiter? Is the company’s founder from your hometown?

What if the recruiter initially responded to your application, but hasn’t followed up with your subsequent messages?

Be persistent. “If you have reached out to a recruiter and they have missed a window of time in getting back to you, try a different way of getting in touch with them,” Jeff added. If you emailed first, try a LinkedIn message next week. If LinkedIn doesn’t work, try a phone call.
…But don’t be a pest. Desperation stinks. “Don’t badger the recruiter multiple times during the same week,” Jeff added. This can be a turn-off for recruiters and may lead to them losing interest.
Don’t be negative. If the recruiter isn’t responding to your messages, you may be tempted to reach out to somebody else in the company — maybe even the hiring manager. This is okay, but be sure not to criticize the recruiter. “I’m not going to want to work with someone who is going to throw one of my current colleagues under the bus,” Linda said.
Know your worth. Is the recruiter avoiding your calls and leaving your emails unopened? These could be clues that they are part of a toxic work environment. “If the recruiter has completely ghosted you, maybe that’s an indication that you do not want to be a part of this team or company,” said Jeff.

How to Supercharge Your AI Job Search With Inside Connections

One of the best ways to get ahead of the competition is through personal connections. Here are some tips for building and using a professional network.

Start with your existing network. “If you know other people who work at the company, use them to get information about the role,” Jeff suggested. “Ask them to put in a good word about you to the recruiter or hiring manager.”

Make new friends. If you don’t have any existing connections, use LinkedIn to find employees who work at the company. Don’t reach out randomly, however — Stuart in accounting isn’t going to be much help landing a machine learning role! Look for people who work on the team you are trying to join. “Send them a connection request with a personalized note that mentions your interest,” Puneet said. Once you have made a connection, ask them to pass your resume (along with a good word) to the hiring manager or recruiter.

Network in person. Attend AI events, tech talks, or open house nights in your area. Check your target company’s blog; it may be hosting one of its own. “Recruiters are much more likely to engage with your application if they have seen you in person at a networking event,” Jeff observed.

Think locally. If you have a job, but it’s not in AI, consider asking around to see if any teams or departments at your company are looking to staff up in machine learning or data science talent, Jeff said. Even if there aren’t open roles, you may have an opportunity to shadow engineers. This shows initiative and you are more likely to be remembered when a position opens up.

How to Prepare for a Technical Interview in Machine Learning or Data Science

If a hiring manager likes your resume, they may ask you to participate in a technical interview. Preparing for a technical interview is much like preparing for an exam. It pays to study and practice.

Study: The first thing to do after getting called back for an interview, Nikita said, is ask the recruiter or hiring manager what technical expertise the company generally expects new hires to have. Ask friends within the company about the role. They are likely to have an insider’s view of what skills the hiring manager is looking for.

Practice: Once you have an idea of which skills to focus on, practice coding. Puneet recommends registering with Leetcode, which has a web interface that lets you practice specific coding problems. Many companies use Leetcode or similar services like CodeSignal and HackerRank to test applicants’ skills. You can prepare ahead of time by reading FAQs for those services.

How to Prepare for a Verbal Interview in Machine Learning or Data Science

Hiring managers and team managers use the verbal interview to see if your personality and views on work will be a good fit for the team.

The best way to prepare for a verbal interview is to practice. “You can search Google for lists of common interview questions, then find a couple of friends and ask them to help you,” Linda said. Encourage friends to ask unscripted follow-up questions. Focus on communicating clearly and answering questions by drawing on your own experiences.

Puneet recommends Blind, a website where people post anonymously about their experiences with companies. Type the company’s name plus “interview” into the search bar, and you may find first-hand information about applying to that company.

How to deal with getting rejected from job applications

Have you ever been excited about a job prospect, only to feel crushed when it slips through your fingers? You aren’t alone. “Everyone goes through rejection,” Nikita said. “I have personally been rejected thousands of times!”

Take the effort to learn why you were rejected. “Was it your application? Your resume? Your LinkedIn profile? Your interview?” Nikita said. “If you can’t figure this out for yourself, ask somebody whose opinion you respect to help you figure out where you might be going wrong.” If you feel comfortable inviting this person out for coffee or tea, do so. You will get invaluable insights from the in-person interaction and ability to ask follow-up questions.

Once you have isolated the likely causes, focus on improving in those areas. “Think about it from a software perspective,” Jeff suggested. “Nobody’s code is perfect the first time. You have to keep iterating.”

Sometimes, however, a company rejects a candidate for something outside their control. “By the time you send in your application to a listing, the hiring manager may have already interviewed a person they really like,” Puneet noted. Or they might have decided to fill the role internally. In these cases, the recruiter and hiring manager aren’t as motivated to consider your resume.

“The most important thing is to not lose hope,” Puneet said. Nikita agreed: “You have to understand that you will get rejected. You can be sad for a little while, but don’t let it keep you down.”

What Will the AI Hiring Landscape Look Like in the Future?

The AI industry is evolving, and it’s tricky to predict the future. Still, Puneet described up-and-coming roles he expects to see big demand for.

ML Ops: This role streamlines the process of building, maintaining, and monitoring machine learning products.
Machine Learning Quality Assurance: This role tests AI-based software to ensure its output meets standards.

Annotation Engineers: These professionals label the data used to train and test machine learning models. They must understand how the data is to be used to ensure their annotations are relevant and accurate.

Conclusion

In summary, the secret to getting your AI dream job isn’t simply to load up your resume with buzzworthy skills. It also comes down to how you present yourself. Your application should be an honest reflection of your experiences. Don’t oversell yourself, but also don’t hold back when it comes to emphasizing all the cool things you have done (especially those that pertain to the job listing). Be proactive about reaching out to recruiters and get to know people inside the company. Be courteous. Above all, know that you will probably fail at least a few times. But if you are persistent about understanding why you fail, there is nothing that will stand between you and your dream job.

For more great AI career advice, watch the full video from “DeepLearning.AI’s Hiring Secrets from Technical Recruiters” event here:

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