You struggled for months on your machine learning career path to find a perfect machine learning job opportunity as great as this one. This is it. This is the machine learning job you have been dreaming about all the time. It’s time to create that first impression with a good resume but you’re unsure of how to make a resume for machine learning jobs that will catch the attention of the hiring manager quickly.
Hold on to your Zettabyte scale writing speed for a minute, and think of this: Everybody is writing their machine learning resume around novel ML tools and technologies. TensorFlow, Amazon Machine Learning, Apache Spark MLib, Mahout, etc are consuming a lot more space on a machine learning engineer resume than the challenges or achievements. You must be enticed to change your name from “Bran Stark” to “TensorFlow Bran Stark”. But, hold on the Director of Machine Learning at your dream company is skeptical about the machine learning tools and technologies as they constantly are evolving. All he wants to see on a machine learning resume is what business challenges you’ve faced and how you solved them using your machine learning expertise.
How to write a good resume? In this blog, find out how to write an effective data science resume that will get you your dream data science job in 2020.
Moreover, recruitment does not start with the Director of Machine Learning of the company. Rather, a hiring manager first screens the resume and then passes it on to the Director of Machine Learning if you have a tick mark for most of the checkboxes as per the machine learning job description. Get it now? Your resume has to pass both of these for you to land a machine learning interview call. Does it look like a lot of effort? Don’t worry, this article will guide you on how to make a resume for machine learning jobs perfectly.
Your Go-To Guide How to Make a Resume for Machine Learning Jobs
Though hundreds of professionals write it, not everybody knows how to make a resume for machine learning jobs perfectly. What does this mean? Recruiters are bombarded with piles of machine learning engineer resumes that look exactly the same. And yours is somewhere in the middle of that pile, waiting to be looked at. How do you stand out? Breathe easy, and follow these tips –
- Choose the Right Header for the Machine Learning Resume
Do not underestimate the header section of your resume as this is the first thing a recruiter sees on your machine learning resume. Make the most out of it. Ensure that the header section has the most prominent details like Name, Job Title, Phone Number, Email Address(professional-sounding email id and not your current work email), and a clickable GitHub link, LinkedIn profile link or any other project portfolio or website link works well for a perfect resume header. Do you know that 76% of resumes are discarded just for including an unprofessional email address? Make sure your email address in the header includes your first and last name and “@gmail.com”.
Let’s suppose that you are a Data Analyst at Walmart, and suppose that your resume header appears something like this –
This might seem to be a good header for your machine learning resume but there are several important details missing in this resume header. Now look at the below resume and see how making small changes to the header can make a great impact –
@ firstname.lastname@example.org github.com/stark2020 Mumbai, India
Did you notice the GitHub link in the header? This gives the Director of Machine Learning pretty good insight on how you code and helps build a real connection. Always make sure you add your personal portfolio, stack overflow, GitHub, or website link to your machine learning engineer resume header. So far so good, you have created the best first impression.
- Nail Down Your Professional Summary Statement like an Elevator Pitch
A professional summary statement on your machine learning resume should demonstrate your unique value through diverse skills and achievements. It gives the hiring manager a sneak peek into a job seekers’ machine learning expertise.
Just imagine you are in an elevator along with the hiring manager who has the key to your dream machine learning job. How will you sell your machine learning skills in that 30-second elevator ride? Consider mentioning about your strengths, technical skills, and achievements that are unique to you in no more than 5 lines or 5 bullet points to let the hiring manager know what value you can add to their organization. For a machine learning resume, the professional resume summary must be specific and should portray a clear picture of your machine learning skills to the prospective employers but that does not mean you include every machine learning tool and technology that you have used.
Machine Learning Engineer Sample Resume Summary
Big Data Specialist experienced in diverse ML tools and technologies. Looking for a role as a machine learning engineer in an MNC where I can explore my skills.
The above resume summary tells nothing specific about your machine learning skills and is a way too common. Here’s another one that is for sure going to pique the interest of the hiring manager to make him say “Wow”! for a number of good reasons –
6+ years experienced machine learning engineer with success in developing accurate and performant predictive models and algorithms for diverse industries. Highly proficient at Classification & Regression, NLP, Deep Learning, Web Scraping, Data analysis, and Data Visualization. Passionate data analyst with the ability to apply various machine learning techniques to solve real-world business problems.
When hiring managers look for machine learning engineers they see that there are very few professionals who have 5 or more years of experience. So, when the experience is listed upfront in the summary, it helps them. The next important thing that is likely to hook the hiring manager to your resume is that it talks about your real passion for machine learning by highlighting the proficiency in NLP, Deep Learning, Classification, and Regression.
- List Key Machine Learning Skills that you have Experience With
The key skills (technical + soft skills) are an important section of the resume and any software tools, technologies, programming languages that you have experience working with should make up your key skills section. Your machine learning resume should not look like a dictionary exploded all over with ML tools and technologies. Make sure you don’t include every invented machine learning skill in the resume.
Pepper all your ML skills listed in this section throughout your work experience section to make the hiring manager believe that you’re good at what you claim and endorse.
Top Technical Skills to include on an ML Resume
- Algorithms & Data Structures
- Programming Languages -Python, R, Java
- Math, Statistics & Probability
- Software Engineering & Design
- Machine Learning Algorithms – Clustering, Classification, etc
- Model Development
- Data Visualization – Tableau, QlikView, etc
- Database Skills – Oracle, MongoDB, Cassandra, SQL
- Data Mining
- Web Scraping
Top Soft Skills to include on a Machine Learning Resume
- Team Work
- Time Management
- Problem Solving
- Business Acumen
- Domain Knowledge
You can explore our career builder machine learning program to learn how you can attain a high level of proficiency with all the above-required machine learning skills in 6 months.
- Include Relevant Certifications on Your Resume
Certifications are the best way to endorse your machine learning skills to prospective employers than any amount of action words. If you have achieved one or more machine learning or related certifications, include each certification in reverse chronological order, beginning with the latest one. Remember not to include irrelevant or expired certifications as they will not appeal to the prospective employer in any way.
Below is a sample format that shows the best way to include the certifications –
- Machine Learning Certification | Springboard India | July 2019 – December 2019
- Data Analyst Certification | Springboard India | January 2019 – June 2019
- Describe your Work Experience in a Cause and Effect Relationship
Don’t just fill up your resume with your past analytics or machine learning job responsibilities using tech buzzwords. Your prospective employer by now would have already read thousands of resumes with responsibilities that are not backed up with any concrete numbers. Instead, make use of bullet points to talk about the impact you have made as a machine learning professional to leave a strong impression on the employer and telling them how you made a difference.
Every bullet point under the work experience section of the Machine learning resume should have a cause-effect relationship i.e. for each task accomplished, mention the outcome of the same in concrete numbers. Something like –
- Built recommendation engine with 99% accuracy to help the company increase sales by 50%.
- Developed a machine-learning algorithm to understand customer behavior leading to 90% success in targeted marketing.
- Built dynamic pricing model using diverse machine learning techniques to maximize profits.
All done? Great. I’m sure your machine learning resume is going to be truly spotless if you follow these guidelines. You now have a great resume for machine learning jobs that’s ready to be sent to your prospective employer.
Let’s Wrap It Up with the Key Takeaways
- Make your resume header stand out with links to your machine learning projects portfolio.
- Let your machine learning resume summary speak for your passion and achievements.
- Make a separate section for your skills and certifications.
- List your work experiences like a cause-effect relationship.
So, are you all set for a career in machine learning? Explore Springboard’s Machine Learning Career Track Program to know how you can upgrade your skills and pursue a rewarding career in machine learning. The Career Track is a self-paced, 1:1 mentoring-led and project-driven program that comes along with a job guarantee.