Shotgun vs sniper rifle approaches in job search

Shotgun vs. sniper rifle in job search

Image source: Kurtz graphics

When I first started out, I had started with what I'd describe as the shotgun approach - scouring job boards for openings, applying for tens to hundreds of job postings online, and most of my applications receive no response whatsoever. In retrospect, this was not totally surprising - US was still clawing its way out from the last recession, and it's unlikely that my resume (with a clearly foreign-sounding name) would stand-out among hundreds of applicants. All the actual job leads that I had (and the job offers I got) were sourced through my professional networks.

Since then, I've been adopting a sniper-rifle approach in my job search. What that may look like:

1. Identify sectors and/or companies that you're interested in, and keep pulse with the industry developments. How to go about doing this can vary a lot, depending on the field you're interested in, so I will not attempt to cover it here. (If you happen to be interested in climate tech like I am, I wrote a separate article about How to keep up with climate tech developments.)
2. When you've identified a short list of companies that you're interested in (whether they have job openings or not), you can leverage your professional network to identify company insiders to conduct informational interview with. We'll talk more about it below.
3. As you came across job openings at targeted companies, ask those company insiders you have connected with and ask if they're comfortable putting in an employee referral on your behalf.

Importance of networking and connections

In all my job search process so far (four jobs in industry since PhD), I haven't had a single interview from the company that I applied to without some form of connections. I hope that gives you a better sense of how important connections are.

Where do you get the connections? You might think that, as an aspiring data scientist, you don't know anyone in this new industry. While that might be true, you are bound to know someone who know someone in the companies you are interested in. The way to find out is through LinkedIn. Assuming you identified the company you are interested in, but you have no 1st-order contact in that company, you can search that company in LinkedIn and find out if you have any 2nd-order contact in that company. Then, email your 1st-order contact for an intro. This works much better than cold-emailing people. When you actually get the chance to talk to the company insider, treat this as an informational interview. Offer to buy them lunch or coffee if the company is close (not recommended during COVID-19), or give them a phone call if the person is far away. Things you could ask them about include: how did you find this job, how do you like the company, what do you work on in general, where do you think the hiring situation is going to be in the next couple of months, etc.

Another way to make connections includes attending data-science related conferences to get to know people. Identify the companies or people that you are interested in, find out if they are attending meet-ups / talks, and just go ahead and introduce yourself. Don't start the conversation by "are you hiring"; you can ask a few educated questions about their talks or posters, asking about their approaches and perspectives in the particular field, then ease into self-introduction and ask for job opportunities. You are your own advocate – if you don't go out and talk to people, you will not be able to make connections.

Professional networking sites in job search

I employ several different professional networking sites in job search – this is not limited to data science, but applicable to technical jobs in general. Here is how each social network / website complement each other in my case:

1. Indeed: you can set up job alerts here; this is a very good job aggregator, and you can set up the job alerts so that it will be delivered daily to your email. I set up alerts for "data scientist" jobs in San Francisco bay area, that would be a pretty wide net in itself. If you have companies that you are particularly interested in, you can set up company specific alerts such as: "Company:(Tesla) (Scientist OR Engineer) jobs".
2. LinkedIn: I use LinkedIn to find connections after I narrow down on a company that I am interested in. From there, I try to get informational interview from insiders; if timing is right, I can also ask for job referrals. See the section on "Importance of Networking and Connections", above.
3. Glassdoor: Glassdoor let employees publish anonymous reviews of the employers. I use this website when I am doing research on the company that I am interested in, often before the information and on-site interviews. Check out both the Company Reviews and the Interviews to get to know people's experiences.
4. AngelList: this is a job posting site that is more focused on startups. You can establish a profile there, and browse relevant job openings.

Where should you work?

Even before you start the interview process, you could use certain criteria in deciding which companies you want to apply. I thought about this question a lot during my job search interviews. My most important criteria at that time was that

1. I want to care about what the company is doing.
2. I want to optimize for my own learning opportunities in hands-on data science.

Near the end of my job search in 2015, I came across this article by StitchFix, Advice for data scientists on where to work, that presents very good set of criteria:

1. Work for a Company that Leverages Data Science for its Strategic Differentiation
2. Work for a Company with Great Data
3. Work for a Company with Greenfield Opportunities

Anyway, this eventually boils down to personal preference, and you may be able to find out more about your own preference by talking to people. But you should be open-minded about learning what opportunities are available. For example, before I started my job search in 2015, I hadn't imagined that I would be working for a credit card company! But as I have gotten to know more about the company and its mission and have spoken with the team, I realized that the opportunity fit the criteria very well. If I hadn't kept an open mind and be willing to learn more about prospective employers, that job opportunity wouldn't have happened.

Further reading

Much of the contents from this article were adopted from another document From materials scientist to data scientist, in which I had wrote about my experience transitioning from a materials scientist to a data scientist in 2012-2015. Be forewarned that the article is looooong... apparently I had more time (and more things to say) back then!