machine learning resume

Why the future is in favor of machine learning engineers

Artificial Intelligence (AI), machine learning, deep learning, and data science specialists are professions that are representative of the 21st century. Today, whenever you drop them into conversations, you’re most likely to hear them being described as either the coming of the apocalypse or of new revelations, depending on the narrator. What’s certain though, is that AI can have a huge impact on the human race. Fact is, we’re heading towards a future wherein technology can supposedly understand us better than we do ourselves, and this is made possible by machine learning and AI.

According to Linkedin’s 2019 report, job openings for machine learning have grown 96% with $182,000 being the median base salary per year —meaning, machine learning engineers are highly sought after. Another study conducted by Analytics India Magazine reveals that there are more than 78,000 job vacancies in the Machine Learning and Data Science fields in India.

Further Reading:5 Useful Resume Tips to Help You Get a Foreign Job from India

Machine Learning and AI were heavily discussed at the recent Davos 2020. One message rang clear at the forum— AI, machine learning, deep learning, and data science specialists have increased exponentially in recent years. Machine learning has been radically changing our work landscape today. In fact, it is being touted as a tool that will help propel us into the future, transforming almost each industry it passes by along the way. Robotics and artificial intelligence will permeate almost every aspect of our daily lives by 2025, and also comes with huge implications for a range of industries such as health care, transportation and logistics, customer service, agriculture, finance, and home maintenance, among others. Moreover, the tech giant Accenture believes that AI technology can boost businesses’ productivity by 40%. If these don’t guarantee a brighter future, I don’t know what will. I hope this piece of news hypes machine learning engineers up.

data scientist resume
5 Tips for Writing the Perfect Machine Learning Resumes

5 tips for writing the perfect machine learning resumes

Resumes are much more than a collective hodgepodge of skills and experiences, so you should organize them accordingly in order to tell a persuasive story with you as the protagonist. Keep it concise and to the point while pointing out why you’re the best candidate for the job. On average, it only takes HR managers at most 30 seconds to filter resumes. So if you fail to impress them in those precious 30 seconds, you can kiss your opportunity goodbye. So how do you pique their interest? Here are five tips to help you entice the HR managers with your impactful resume:

1. Choose a template that best showcases your experience

    Unlike creative-driven positions, clarity is key to a good machine learning resume— highlight your experiences clearly and concisely. Simply put, your resume should be easy to read; bonus points if you can make it aesthetically pleasing, but it shouldn’t be a priority. Generally, all resumes include the information I’ve listed below. Even if the order can be adjusted accordingly, I’d jot down the most important ones on the top if I were you. It’s actually not that much different from resumes written for other fields.

    • Header— include the basics like your name, address, and contact information.
    • Personal Summary— 3-5 sentences answering the question, “Why should you be hired?” should suffice.
    • Relevant Experience or Projects— Highlight your accomplishments and duties in your previous jobs. Better yet, a Github profile or a Kaggle profile can definitely help boost your chances.
    • Education or Certifications— Be it online courses or a university degree, list them all down to let companies know how much effort you’ve put in to stay ahead of the game.
    • Skills and Languages— Take inventory of your skills and credentials that you haven’t yet explicitly listed, like programming languages such as Python, C, C++ or Java; NLP, etc..
    • References— Include the contact information of two or three people who can provide insight into your work ethic and vouch for you. Professors work too.

    2. Show, don’t tell. Prove that you walk the talk

      As you may have read in other articles or experienced in real-life, too many superfluous adjectives (and verbiage in general) is not good. So, instead of jamming a three-hundred-word essay into your resume, let me help by showing you what a decent resume should be like.

      Resume Sample:Machine Learning, AI, Data Science

      data scientist resume

      Resume Sample:Data Mining, Machine Learning, Distributed System, Web Crawling

      data engineer resume

      These two examples are from CakeResume, both of top-notch quality compared to examples elsewhere. As you can see from both versions, a lot of concrete metrics and numbers are listed. These examples are also straight to the point, listing out experiences with big data tools, past projects, and published papers; They are also organized and not messy. Basically, these two tell what the writers have worked on and how well they’ve performed!

      Check out more Machine Learning & Data Analyst/Scientist/Engineer Resume Samples!

      3. Customize your resume to the job descriptions

        A one-template-fits-all resume might save you a great amount of time, but you’ll likely end up spending more time applying for more jobs. You should customize and tailor your resume for each recruiter. But how do you make sure you’re efficient with the time spent customizing each of your resume? Easy. You can build a two-or-three page long outline that lists all of your certifications, courses, and projects— essentially everything that was covered on the previous tip. Next? Go through the job descriptions of your coveted jobs. Want to look like the perfect candidate who’s sure to be a competitive high performer but also relatable? Read the job descriptions carefully and match the tone. Clarity and coherency should always be your priorities. Remember that your resume will be first screened by the HR manager before being sent to the higher-ups, so using a lot of jargon may turn the initial screeners off. If you see some technical terms on the job description, then feel free to sprinkle in some to add more specific details. Once you know what they want, you can save a great amount of time in convincing HR managers that you’re the one they’re actually looking for.

        4. Structure, structure, and structure

          Aside from keeping your resume clean and simple— I personally prefer a minimalist style with a touch of clarity. The secret to condensing your whole life story and accomplishments into a single A4 page is in organizing it well. You can separate each category into different sections: past experiences, completed projects, awards received, etc. When it comes to your work experience, you can opt to list them chronologically. But remember, don’t list them all. You should only choose to showcase your most impressive works. That being said, you’ll need to strike a balance between quantity and quality. Most importantly, bullet points can help you tell a story better than huge chunks of texts.

          5. Don’t downplay your success

            Here’s a question for you, when does imposter syndrome strike the hardest? A. Meeting your partner’s parents, B. Writing or updating your resume, or C. Leaving your comfort zone. Personally I’d choose B. If you’re Asian, please throw humbleness out of the window for a second—You can have it back once you finish writing your resume. Then, go ahead and brag about your accomplishments as much as you want. A word of caution, honesty is the best policy: You can highlight your exceptional accomplishments, but don’t lie about things you’ve never done.

            data analyst resume
            3 Tips for Your Version 2.0 Resume

            The final touches on your version 2.0 resume you’re onto the home stretch!

            Here are a few tips when you’re writing resumes in general. These are equally important even though they’re not specifically for machine learning engineers. So, grab a pen and note these down!

            1. Use the active voice

            Passive sentences sound awkward and take up too much real estate on your page.

            2. Have your friends or someone you trust look it over. 

            Once you’ve dotted all the i’s and crossed the t’s, you can find someone reliable to check your resume for you. If they’re not familiar with machine learning, that’s fine, you can get really honest and useful advice from outsiders as well.

            3. Leave some empty space

            It can be suffocating to read a resume filled to the brim with words so resist the urge to fill every square inch of your resume with text.

            Closing Thoughts

            To the kind souls who are still with me, I hope you’re finding this article useful. Companies today are hard-pressed to find good machine learning talent. For certain roles, you might need a specific skill set; some skills though, are quite universal. Needless to say, companies are looking for talents who already possess a diverse set of skills, knowledge of different theories, and programming ability. Some other basic requirements also include a good understanding of mathematics, analytical thinking skills, and problem-solving skills. With all that said, it’s time to get working and tailor your resume! If you’re looking for more inspiration, CakeResume is not only easy to use, but it’s also a treasure trove for an excellent array of sample resumes.

            Congratulations! You have built a strong resume. Next step is all about writing a professional cover letter.