How to Get Data Science Interviews: Finding Jobs, Reaching Gatekeepers, and Getting Referrals
In this post, the author shares what to do to get job interviews efficiently. Find answers to these questions: Where should I look for data science jobs? How do I reach out to the gatekeeper? How do I get referrals? What makes a good data science resume?
Getting interviews is naturally essential for many jobs and data science jobs are no exception. Although resources on this topic are certainly not lacking, practical and actionable advice is rare. Going into my job search I knew that over 70% of job seekers find employment through some form of networking. I knew that I should “contact recruiters and build up my network to break in the field”. But even knowing this I was still left with an important question: how?
The first time I looked for a data science job was when I was about to complete grad school in Feb. 2017. I tried everything I could to get interviews including…
- Hundreds of applications on LinkedIn, GlassDoor, and Indeed
- Reached out to people (alumni, people with common friends) on all social networks for referrals
- Researched on Google and tried everything (except creating my own data science job) that’s recommended in articles such as “Top 10 Ways to Get Data Science Interviews”, “5 Tips for Getting a Data Science Job” and “How to Get a Data Science Job: A Ridiculously Specific Guide”.
- Spent numerous hours creating, formatting, and refining my resume
After job searching with the commitment of a full-time job (i.e. spent at least 8 hours a day) for three months, I finally got my first interview opportunity through AngelList.
The second time I found myself searching for a data science job was when I was laid off by my then startup company in Dec. 2018. This time around I got 10 interview opportunities within a month using 50 applications and 18 referrals (spent less than 30 hours in total). This enormous difference was not because I had job experience. In truth, I was not much more competitive on the job market the second time. This drastic change was because I did it all wrong the first time! This was not because the articles on getting a data science interview were misleading or incorrect, but they only told me what to do not how to do it.
In this post, I will share not only what to do (only 3 ways rather than 10) and how to do them to get interviews efficiently. You will find strategies, scripts, and other free resources in this post which will help you be methodical and organized in your job search. Specifically, this blog answers these questions:
- Where should I look for data science jobs?
- How do I reach out to the gatekeeper?
- How do I get referrals?
- What makes a good data science resume?
Before we begin, are you more of a video person? Watch this YouTube video instead of reading.
The Three Approaches
There are three ways to get interviews: raw application, contacting the gatekeeper, and getting referrals. Raw application means simply submitting your resume to job openings. Although this is an easy method, it also tends to have low efficiency. Contacting the gatekeeper can be more effective but requires a bit more effort. Getting a referral is the most effective way, but it also takes the most amount of time and effort (assuming that you don’t know anyone willing to refer you yet). The below diagram gives a visual of the three methods in terms of effort/time and effectiveness.
Raw Applications: It Matters Where You Search!
When we first think of job boards to check in our job search, we often turn immediately to popular online job boards, such as LinkedIn, GlassDoor, or Indeed. The first time I looked for jobs, I submitted hundreds of applications on LinkedIn but got zero replies. In fact, some of my friends who are making career transitions also find themselves getting no replies on LinkedIn. When my second job search began, I did not submit a single application on LinkedIn.
Why? The problem was that for each job post, hundreds of applicants applied within a week. If you rely solely on these popular job boards, the chances that you will get a response are slim. You are competing against a mountain of candidates!
Unfortunately, the fact is that in recent years, LinkedIn has become a platform that serves recruiters who want to reach potential candidates with an exact experience and qualifications. It’s not a platform that serves job seekers with a lack of experience because you have to compete with hundreds (sometimes thousands) of applicants for a position.
One way to get around this problem is to apply on less popular websites. Use websites that are not yet mainstream. Skeptical? Edouard Harris does an excellent job of explaining why “companies pay more attention to applicants who apply through less known channels” in his blog post. Here are some websites that I and people I know have tried and have proven to be helpful. Some are even specifically data science-related.
- Kaggle Jobs
- KDnuggets Jobs
- Data Science Central — Analytic Talent (US-centric)
- DataJobs (US-centric)
- Icrunchdata (US-centric)
- TripleBytes — Data Science
In addition, there are a few smaller online job boards that have higher response rates than the three largest.
- AngelList — I got my first data science job through it!
- Github Jobs
Whenever applying on any job board, try to send a personalized note to the person who is hiring. Research the company (using the company website, Google, and Glassdoor) and explain why you will be the best fit for that position. Personalization will greatly increase your chances of getting a response.
Reaching out to the Gatekeeper: Say Hello to More Interviews
Even though the websites I recommend tend to have higher response rates than big job boards, it can still take weeks for recruiters to respond since they always have a pile of resumes to review. Now you would want to try the second method — reaching out to the gatekeeper directly. I was able to get responses much faster using this method than raw applications.
A gatekeeper refers to a technical recruiter or a data scientist in the company in which you are interested in working. Big companies typically have technical recruiters that are dedicated to hiring data scientists, but the data team at smaller companies may be more self-contained. If you can win over the gatekeeper, it could allow you to move on in the job hiring process.
Here are a few steps for winning over the gatekeeper:
- Compile a personal list of companies in which you are interested (even if there are no openings at the moment)
- Find the contact info of the gatekeepers (you may find multiple gatekeepers in the same company) by going to the company website or use LinkedIn
- Develop an email pitch that will grab the attention of the gatekeeper
- If you don’t hear back within a week or so, send a follow-up email and continue following up until you get rejections
An email pitch should be concise and complete. Your email might be forwarded to a different person and no one is going to copy and paste multiple emails for you so the pitch needs to have everything. A good email pitch contains two parts:
- The first part is a quick intro (2~3 sentences) which includes your intention, a brief description of your experience, and why you are interested in that company. Again, doing some research on the company will greatly help to express your interest in a specific manner.
- The 2nd part is about your most impactful project. Make sure this is interesting, readable, and shareable.
To make things simpler, here is a template.
If you get a response within a few days, great! But if you don’t get it within a week or so, send a follow-up email to the gatekeepers. Based on my own and my friends’ experiences, the response rate to the follow-up was higher than the first email. This is the template I used for the follow-up email:
While you can certainly use both templates as a starting point, do NOT copy them. If the gatekeeper sees exactly the same email template, it will leave a negative impression. You can change things. You can make it longer or shorter. Just remember that the main idea is to show your interest and send everything that’s necessary.
Getting Referrals: The Best Way to Interview at Your Dream Company
When looking for jobs the first time, I reached out to alumni, people with common friends, and even random people to get referrals. However, I ended up not getting any. In contrast, the second time I looked for jobs, people within my network told me they were willing to refer me before I even asked.
Over time, I have learned some misconceptions about asking for referrals. Getting a referral is to get someone familiar with your work to give a glowing referral to their companies. It is NOT hassling strangers you found on LinkedIn or somewhere else. Besides most of the time, the latter approach doesn’t work.
Getting a referral differs from reaching out to the gatekeepers in that it starts with building relationships with people who work at tech companies, no matter what their professions are. Whether they are a product manager, software engineer, product designer, or anything else. A relationship goes a long way. Haseeb Qureshi has a great blog post on breaking into the tech industry. The part on networking especially is brilliant. Qureshi says …
… people hate it when you ask them for a job.
Give you a job? Why? They don’t know you. Why would they give you a job? Why would they even waste their time on you?
The power of informational interviewing is that instead of making it about you, you make it about them. People like to talk about themselves. They like to teach others. They want to help. But they don’t want to be pestered by strangers for favors.
If you keep doing this, people will see your curiosity and your genuineness. They’ll believe in your story, and they will want to refer to you.
It may sound like lots of work especially when you haven’t done it before, but there are actually only 4 steps to build a relationship correctly:
- Buy people coffee. This may not be easy to do during the pandemic, so you could ask for a brief zoom chat.
- Do an informational interview. You want to show interest in the person and what they do as well as their company.
- Thank them the next day via email and follow up. You don’t want to make a one-off connection. A relationship needs to be maintained.
- Once someone is willing to refer you, you want to make it a smooth referral. Instead of asking “are there any openings in your company”, go look at the company website yourself and find the opening that is the best fit for you. Then prepare an email with your resume, the job link, a description of your experience, and an explanation on why you are a good fit.
These exact steps led me to 18 referrals in my second job search. If you follow all these steps, you will get strong referrals! This will not only help in your first job search but in future searches as well.
Below is a cold email template that you could use as a reference for the first outreach. The main point here is to make it personal and show your genuine interest.
Again, the world is small. Don’t copy the exact script. Take the time to customize your message because, in the long run, it is worth the effort.
Qualities of a Great Resume
Depending on your goal and availability, you may choose one or more of the three methods to get interviews. But no matter which methods you use, a good resume is key. This is the thing that turns the table with recruiters.
A resume is a summary of your achievements. It should be brief, so it’s not a place to showcase everything you know or can do. Adding too much content only overwhelms people with unnecessary information. Therefore, in your data science resume, you want to highlight the most important things related to data science, such as work experience, training, and relevant skills. When a recruiter or a hiring manager sees your resume, they should immediately feel that you have lots of experience in data science and that you are a qualified candidate.
Here are a few rules on writing a resume that I have created after testing my own resumes, talking to many recruiters and hiring managers, and reviewing other people’s resumes.
Avoiding red flags:
- A good resume is ALWAYS 1 page long. Recruiters and hiring managers receive many resumes a day, and they typically have less than a minute to skim through someone’s resume to make a decision. If your resume has multiple pages, they likely won’t even get the 2nd page.
- Don’t have typos. Have a friend do a peer review to make sure there are no typos or grammatical errors before sending it.
Improving your resume:
- Be selective and curated. Start with your highest-impact experience, projects, and skills. In the experience and projects sections, make sure that each item has no more than 4 bullet points. The first bullet point should always be the most impactful point measure by business metrics, such as improvement in user retention or conversion, reduction in bounce rate or cost, etc.
- Be concise and clear. Your resume demonstrates your communication skills, and to show that you can communicate effectively you should take care to remove redundant phrases and rearrange verbose expressions.
Making your resume stand out:
- Don’t tell, show the numbers and metrics. When describing your experiences and projects, show your contribution measured by business metrics. Show the size and type of data that was processed and cite the exact technologies you used. Being specific makes your resume more trustworthy.
- Don’t send one resume for all positions. It’s always worth adding customized tweaks to it for each application. At the very least, if you observe important keywords and skills mentioned in a job description, use those in your resume and highlight your skills in those domains. It is not okay to make things up, but it’s totally fine to restructure your skills and experiences to fit the context of a job description.
At the end of the day getting data science interviews is difficult, especially for beginners. It can be discouraging when you don’t get any response even when you have been working diligently for months. Hopefully, this article has made things clearer for any aspiring data scientists out there who are in the process of landing a job. If you want more advice feel free to contact me here, I would be happy to help!
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How I Got 4 Data Science Offers and Doubled my Income 2 Months after being Laid Off
The Secret to Getting 100% Onsite-to-Offer Rate
Bio: Emma Ding is a Data Scientist & Software Engineer at Airbnb.
Original. Reposted with permission.
- How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off
- How to Get a Job as a Data Scientist
- How to Get Your First Job in Data Science without Any Work Experience