We have all seen them: Junior Data Science job postings requiring a Masters degree and 5 years of experience. Job descriptions that require 4+ years of experience with a tool that was developed 2 years ago. “Entry level” roles that require data engineering, modeling, machine learning, and advanced visualization skills. 

These days, hiring managers seem to want mid-level talent for entry level data science roles, but in reality, the market necessitates more flexibility. McKinsey Global Institute predicted that “by 2018, the United States will face a shortage of up to 190,000 data scientists with advanced training in statistics and machine learning as well as 1.5 million managers and analysts with enough proficiency in statistics to use big data effectively.” Since that 2013 forecast, the industry has experienced exponential growth, with job sites like Indeed and Dice reporting an approximately 30% increase in Data Science job postings from 2018 to 2019.

The jobs are there. The people are not. Companies must be flexible if they are going to grow their data science teams. But how can you, a job seeker who may not meet all of the listed educational or technical requirements, get hired? Follow these three rules: Be flexible, be realistic, and use your network.

Rule #1: Be Flexible

Keep an open mind when you are looking at a job description and have the courage to apply if you meet at least half of the requirements. Don’t let your imposter syndrome get in the way of you sending in an application. Job postings are a wish list for companies – they expect you to bring your skills to the table and learn the rest on the job. Their best candidate will likely meet 50-60% of the listed requirements, including educational requirements. 

Do you meet most of the technical requirements through your Lambda or personal projects but the listing says 3-5 years of industry experience is required? Apply. If a Master’s degree is listed as “required” and you don’t have a Bachelor’s degree but have all the right technical skills, apply.  Your experience and the skills you gained at Lambda make you a great candidate for the role.

“When it comes to hiring Data Scientists, it doesn't matter if the candidate has a bachelor’s, master’s or PhD, we are just looking for smart and eager individuals who want to work in the data science space,” explains Jessica Aspis Wender, Managing Strategy Consultant at IBM’s Chief Analytics Office. 

Your resume should reflect the drive and analytical ability that Jessica mentions, along with being optimized and searchable for the keywords listed in the job description. This will increase the chances that the recruiter will pick up your resume in their applicant tracking system. 

Rule #2: Use Your Network

The only way to guarantee that your resume is seen, however, is by getting a referral through your network. Your first and best resource for getting a referral should always be your LinkedIn connections. Start by categorizing the people in your network. Once you have sorted through your connections and know your contacts’ employers, begin targeting those companies first. Why? Because you already have a connection to someone who can refer you for a job. This person does not need to be in a data science role to give you a referral - if they know you personally or have worked with you in the past, they can vouch for your skills to the hiring team. Additionally, many companies have a policy that employee referrals are guaranteed a phone screen. So, no matter how many of the requirements you meet and whether you have the listed educational background, you will have the opportunity to pitch yourself for the role. 

Once you have exhausted your contacts’ current companies, start searching more broadly. Any time you apply to a data science role online, you should investigate your warm and cold network. Send a LinkedIn connection request to a recruiter and someone on the Data team for each role. Make sure to send a note with your connection request that includes what role you applied to, why your technical skills are a fit, and an invitation to discuss the opportunity further. Through this very brief introductory note, you are giving them your resume, calling out your fit for the position, and demonstrating your soft skills through a conversation request. Now that is a good use of your time.

Real talk: Most cold contacts that you reach out to on LinkedIn will not respond. But, when they do, they are engaged with you directly and appreciate the extra effort. It is this proactivity that will get your foot in the door to your dream data science job, regardless of the listed requirements.

Rule #3: Be Realistic

Research teams at companies often list PhD requirements and hold firm on them since they are looking for candidates who have research experience along with advanced technical skills. If a research job description lists a PhD as required, then move on. This could be a role you target in the future, but likely will not be anyone’s first data science job. The strategies and flexibility described above can be very effective for other entry-level positions and you should not waste your energy applying for jobs that require a PhD. After you’ve gained a few years of experience in other data science roles, your resume and skill set will be robust enough to apply for these types of positions.

Now what?

Get started! Find inspiration in the marketability of your skills and use it as motivation to apply, network, and land that first data science role. Good luck!

Rachel Cohen is a Lambda School career coach with a passion for helping people transition to technical, interesting, and well-paying roles. She has been coaching data science students for 5+ years, with previous experience in career counseling and technical recruiting. You can connect with her on LinkedIn.