With a job demand never seen like before, data scientists are the new best thing. Ever since its conception, data scientists are required in several industries and companies due to the benefits that they offer.
The advantage of efficient handling of large databases is the prime function that sets them apart. Almost all companies have jumped on the technology bandwagon, meaning that there is a whole new set of operations to deal with.
Considering the fact that most industries and companies are constantly searching to expand, the demand for data scientists will only continue to grow. With this increase in demand comes an increase in the number of applicants. Here are 5 tips to perfect your Machine Learning Resume:
In such a fast-paced setting, hiring managers do not have time to spare through thousands of applications that they might get daily.
To combat that, they use a software called the Applicant Tracking System or ATS that sifts through the resumes and selects them based on some criteria, such as:
To have an ATS-compliant data scientist resume, you need to choose the reverse chronological resume format.
Formatting comes into play when you’re writing the professional experience section or internships section. Here, what you can do is follow the reverse chronological order and add the most recent experience and then move backward.
The ATS screens your resume for certain keywords to know if you are fit for the job.
The way they do that is by matching your resume to the job description enlisted by the company and search for common phrases or terms to ensure that you have what the company wants.
While it is important to use keywords, blindly stuffing your resume with keywords is a bit of an overkill. Your plan will backfire when your resume reaches a hiring manager, and they do not want to see resumes have just keywords and no actual content worth hiring for.
In your resume, the summary section acts as a trailer for your resume. Hence, it should have the best pieces of information written in an engaging manner.
Here are a few things to keep in mind before drafting a summary:
- Make sure that it stays within 5 lines
- Include important information and skip out on the irrelevant ones
- Maintain a cause-effect relation wherever possible to exert your problem-solving abilities
- Mention any awards or honors here or make a separate section for it
- Quantify your work functions to give the recruiter solid data to assess your performance by
Your data scientist resume summary is the first step to impressing your recruiter by presenting a good cluster of your accomplishments.
Data Scientist Skills
Data scientist skills are an amalgamation of soft/hard skills and technical skills. Both are equally important, and that makes it all the more crucial to explain both in detail.
Split your skills section into key skills and technical skills for ease of understanding and proper characterization.
Some of the key skills you can mention are:
- Data analysis
- Data visualization
- Predictive analytics
- Project management
- Process improvement
You can group your technical skills into sections and then list the names as given below:
- Programming languages: Python, R
- Big data stack: PostgreSQL, Hadoop, Spark
- Statistics/ML: SVM, Linear/Logistic regression
- Packages: Matplotlib, NumPy, Pandas
It does not have to be the same! You can add and remove key skills or technical skills at leisures after thoroughly assessing your areas of expertise.
There is a tried and true method to list your professional experience, be it part-time or full.
The hiring managers usually search for numbers and major functions while they scan the professional section of a resume, so add them in a way that is readable and easily scannable.
Here are some tricks you can use to get a bomb work experience section:
- Exclusively use a bulleted list of one-liners and resort to splitting an idea into two one-liners if the sentence is too long
- Quantify your achievements and highlight the crucial points
- Bucket the same experiences under one title and use various labels to explain different functions
- Maintain a cause-effect relation to exert your problem-solving skills
Drafting a work experience section can be tricky but try to follow the general rule of thumb, that is making it as concise and informative as possible.
Certifications and Training
More than a degree, data scientist certifications have gained popularity when it comes to gaining expertise. Due to the extensive resources, you can hone data scientist skills through certification courses.
Any training or certification that is relevant to the job description can be added here in the format:
Certification | Certifying authority | Time period
You can add any accolades or honors you received during the time you did the course. If not, you can make a separate section for awards and achievements.
Drafting a data science resume is like categorizing a whole bulk of data into small sections. By prioritizing certain information and organizing them properly, you can have a great data science resume.
Here’s how you can do that:
- Make sure that your resume is ATS-compliant by choosing the reverse chronological resume format and adding keywords that appear in the job description
- Draft a resume summary by presenting a cluster of your best accomplishments and maintaining a good flow of ideas within a maximum of five lines
- Split your data scientist skills into key skills and technical skills as a way to highlight both and delve deeper into both topics
- Add your work experience to your resume in one-liners as a bulleted list, and make sure that you categorize similar work functions into groups and highlight keywords or any quantified data
- Make a separate section to add any certification or training to your data science resume in the above-mentioned format
By following these tips you can ensure that your data science resume is up to the mark and finally bag the job of your dreams.