Having a machine learning resume that shows your relevant skills is important. Discover how to use machine learning resume keywords and format your resume.
When writing your machine learning resume, it is vital to ensure it aligns with the role you’re applying for. Different formats, styles, and sections are available for promoting different skills. What you choose is impacted by your career path, level of experience and education, and industry.
Machine learning is a new, popular field, and you need a resume highlighting your skills and experience in the position context, demonstrating that you meet all the essential requirements.
Machine learning is a relatively new discipline born from the growing focus on automation. It is a form of artificial intelligence using software and algorithms to imitate human behavior, becoming more accurate as it “learns” in the same way as a human. An example of the way we use machine learning is the “recommendations” engines that appear when we make an online purchase based on our choices or how our email inbox filters spam.
A machine learning engineer is a person who researches and creates these codes and systems through specialized programming.
Machine learning engineers are in high demand. In fact, machine learning is one of the fastest-growing in the technology sector. However, a skills gap exists between what companies are looking for and the qualified candidates to fill those positions. Becoming a machine learning engineer requires high educational achievement, relevant experience, personal projects, and specific skills. If you have these, demonstrating them on your resume is essential.
Writing your machine learning resume requires planning and consideration of the most critical parts to highlight to tailor your resume to the position. Tailoring your resume is key, along with choosing an easy-to-read professional machine learning format that allows you to include all the appropriate sections and highlight your skills.
Machine learning is a broad field with roles that span different industries. As a machine learning engineer, you should target a specific industry, such as retail, technology, health care, or financial services. This is useful for establishing a niche if you have relevant experience in a specific industry.
You can target the industry you want to work in through your profile or summary, and throughout your resume by highlighting your relevant skills and experience the industry asks for and using appropriate machine learning resume keywords you find relevant.
Machine learning is a growing field, and the job outlook for machine learning engineers is excellent. The US Bureau of Labor Statistics predicts the industry to grow by 21 percent between 2021 and 2031 , much faster than average.
Resume sections are important because they break up the text systematically so the reader can scan through and find what they are looking for. They also add structure to your resume. With this in mind, the sections you include need to be relevant to the duties of a machine learning engineer and have the expected information in that section.
A machine learning resume will benefit from the following sections as a guide. Remember that adapting the section order to make your most significant assets stand out is best. For example, put that first if your education is more impressive than your experience. You can also add some additional sections outlined further down.
Header: Your header includes your name and contact information, including a link to your LinkedIn profile if you have one.
Summary: Your summary is a short bio summarizing what is to come in your resume.
Experience: This section is critical because it is here that you show you have relevant machine learning experience, listed in reverse chronological order.
Education: List your education here, with your highest qualification at the top.
Skills: Include skills mentioned in the job description for the role you’re applying for.
There is no right or wrong when choosing a machine learning resume format. Select the one that best suits your needs. Reverse chronological is the most common resume type, which focuses on listing your education and experience with the most recent first.
While this can work well, if you are new in your career or transitioning from a different career path, this can highlight your lack of experience, so you may prefer to use a functional/skill-based format. This puts your skills above experience, demonstrating that you have what is asked for before listing your past jobs.
Whichever format you choose, make sure that it is ATS compliant, which is best done by sticking to a simple template without text boxes and color and with lots of white space. Stick to a single professional font, ideally in size 11/12.
Your summary is a short bio (around 4-7 lines) providing an overview of who you are, your relevant skills and experience, and what you can bring to the position. Tailor this section to the role you’re applying for, highlighting the key points that the employer is looking for. This is your sales pitch, and how well you write your summary determines if a recruiter will read on and if you will be shortlisted.
This section is crucial because it is here you show your relevant machine learning experience. This is where you list previous jobs, including company, title, and date, and add details about your duties. Look at the position you’re applying for, identify the essential criteria, and provide evidence. By using job descriptions to help pinpoint what to include, you ensure you increase the ATS compliance of your resume by focusing on desirable keywords.
It’s easy to list responsibilities, but it is achievements that recruiters are interested in. They want to know what you have done that sets you apart from other applicants.
When listing achievements, include results, so it’s easy for the reader to see your contribution's outcome and full value. If you can make these results measurable, so much the better. For example:
“Contributed to developing personalized algorithms increasing usability by 45 percent.”
Some candidates break their experience entries under each position into two sections—responsibilities and achievements. This is a good way of distinguishing the main tasks associated with your job and what you have achieved.
Another great technique is to use the STAR method. Star stands for “Situation, Task, Action, Result.” For every bullet point in your experience section that demonstrates an achievement, think about the situation (your role), the task (the overall goal), the action (what you did to reach this goal), and the result (the measurable outcome).
Machine learning engineers are highly educated. A bachelor’s degree is the absolute minimum requirement, but most have a master’s degree or, more commonly, a PhD in a related field. Education is important, so leverage it beyond obtaining graduate requirements. Include bullet points to show achievements on specific projects, excellent grades, awards, recognition, and scholarships. You should also highlight any local or national honor societies.
Read more: How Long Does It Take to Get a PhD?
The skills section is important in any resume, but specific machine learning resume skills are in-depth, technical, and essential to any position. Many of the skills are very specialized to the industry, so document them in your resume.
To ensure you’ve listed the relevant machine engineering skills, look through job descriptions and include everything essential to tailor your resume and make it ATS compliant.
Technical skills you might include:
Principal component analysis
Support vector machines
Workplace skills you might include:
You may want to include several other sections on your resume, depending on what you’re applying for, your experience level, and additional projects and awards.
Title: You should include a title or headline that sets the scene for what you do. For example, under your header, you may add the title “Machine Learning Engineer” or something more specific such as “Machine Learning Engineering Specialist for Global Retailers.” Doing this adds extra keywords and tells the employer you are already functioning at this level.
Projects: This section is helpful if you need more professional experience or have undertaken personal or school projects. Anything not covered in a paid professional position related to machine learning can go here.
Publications and conferences: If you studied at the PhD level, you might have relevant publications to list and have attended some notable industry conferences.
Professional associations: Being part of a professional association can show you are serious about machine learning and making it a career. These may include The Association of Data Sciences (ADaSci).
Licenses and certificates: Your education may not only be formal and academic. You may have taken some certification or nonaccredited courses or applied for licenses. They warrant their own section, or you can make a subsection within education.
Awards: If you have won any awards, either personally or as part of a team at university or work, this is a great place to show them off.
Volunteer work: If you have had any volunteer roles demonstrating your workplace or technical skills, you can include them under this additional section.
Once your resume is ready, you need a robust machine learning cover letter. Tailor the cover letter to the position you’re applying for, the company, why you’re seeking the position, and why you’re a good fit.
The body of the letter should further elaborate on your relevant skills and experience.. Keep the tone professional and to one page. Focus on what you can offer and finish with a call to action.
According to the US Bureau of Labor Statistics, the median salary for a computer and information research scientist (which includes machine learning engineers) is $131,490 . More specifically, Glassdoor states that a machine learning engineer earns an average of $130,019 .
Machine learning requires a strong skill set, knowledge in the field, and an academic background. If you are interested in starting your career as a machine learning engineer, take a look at this course on Deep Learning Specialization listed on Coursera, which can help you understand deep learning and equip you with the knowledge to develop AI technology.
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U.S. Bureau of Labor Statistics. “Occupational Outlook Handbook: Computer and Information Research Scientists, https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm#tab-6.” Accessed November 10, 2022.
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