This article is a part of our in-depth Data Science Career Guide. To read the other articles, please refer to the table of contents or the links that follow this post.
Once you know the types of jobs you want to apply for, you’re faced with the next data science job search challenge: how and where can you actually find data science jobs?
Let’s take a look at some of the places you can hunt for data science jobs, and what data scientists and hiring managers have to say about them.
Big Online Job Boards
- They list a large number of jobs.
- They often have an “Easy Apply” option that allows you to send credentials quickly.
From the perspective of a job hunter, though, are these truly advantages? The initial ease of search and application isn’t particularly meaningful unless you actually get job offers, and on that front, these sites have some huge disadvantages:
The competition is massive. Because jobs on these popular sites are easy to find and apply for, a single job posting will attract hundreds or thousands of applicants, particularly if it has an attractive salary. That means you’re up against a ton of competition, and it also means that potential employers have to filter signal from the noise. It’s easy to get lost in that shuffle even if you actually are a good candidate.
“Companies post on Indeed and all these giant, massive places,” says SharpestMinds co-founder Edouard Harris. Then, because they get a ton of applications, “they just cull with a blowtorch by eliminating according to nearly arbitrary criteria.”
Many companies avoid hiring from them, especially for technical jobs. There are companies that avoid these sites entirely, of course, but even among those that post there, there is often a preference for candidates who come via other channels (especially personal references). For technical positions, a LinkedIn or Indeed posting may bring in thousands of resumes from candidates who aren’t really qualified, and that means hiring managers will spend more time looking at candidates from other channels, where they’ve typically found a higher rate of qualified candidates.
“A lot of those [sites] turn into black holes,” says Pacific Life data scientist Alyssa Columbus. “Sometimes recruiters even forget to check them.”
They encourage bad practices. Because it’s so easy to submit an application to any job, it becomes tempting to create a “general data science” resume and just blast it at every job posting that seems remotely relevant. This approach will help you send out dozens of applications every day, but the response rate is likely to be abysmal. At a bare minimum, you should be tailoring your resume to highlight particular skills and projects based on the requirements of the job you’re applying for (see our resume article for more on this topic).
In general, we suggest avoiding the big job sites. It’s certainly possible to find jobs there, but given that you’ll be up against a mountain of other applicants — and hiring managers may not even see your great application — spending large amounts of time applying on big job sites isn’t the most efficient way to approach your data science job search.
Data Science Job Boards and Company Websites
Smaller industry job boards and job postings on company websites may be a better option than global jobs sites like Indeed. But it’s worth remembering that here, too, you’re up against a lot of competition. On a popular data science job board, for example, you’ll be competing with fewer applicants, but you’ll still be up against a lot of competitors for entry-level jobs, and each applicant is more likely to be well-qualified.
Data Science Job Boards
If you feel you stack up against the competition, here are some well-liked data-science-specific job boards (note: we have no professional relationship with any of these sites, this is just a list of resources):
- Kaggle Jobs: A solid number of data science and analysis jobs, often with well-known companies, all over the world.
- KDNuggets Jobs - A few new jobs each week, again often at brand-name firms, primarily in the US and Europe.
- Data Science Central - Analytic Talent: US-focused data science jobs, with many big tech brands and at least a few new jobs each week.
- AnalyticsVidhya: India’s biggest data science job board.
- Analytics India Jobs: Another major data science jobs board focused on India with at least a few new jobs each week.
- Stats Jobs: Data science and statistics jobs, primarily in the UK, but with occasional postings elsewhere in Europe and the rest of the world.
- Icrunchdata: A variety of data-related jobs that aren’t all relevant to data science (but you can filter by required skills, which is nice). US-centric but does include some global jobs as well.
- DataJobs: Includes data-science-related jobs and data engineering jobs, primarily in the US.
- StatisticsJobs UK: Statistics, data scientist, and data analyst jobs in the UK.
And a couple of other job boards you may want to make note of:
- GitHub Jobs: While larger and not data-science exclusive, GitHub does list data science jobs for some of the biggest tech brands in the business.
- AngelList: A general board for jobs of all sorts with startups. If you’re interested in working for a startup you can often find data-science-related positions here.
A company’s website can be a good place to find and apply for jobs, but it can sometimes be difficult to tell how recent job postings are (and not all companies keep their websites regularly updated). Whenever possible, it’s probably best to reach out to the HR director, hiring manager, or data science team leader before applying — this way, you make a slightly more personal connection and you can confirm you’re not wasting time applying for a job that was filled a year ago but never taken off the website.
Many job applicants only ever look at online job boards because it’s convenient. But if you’re looking to boost your chances of success, you may be better off taking a much more personal approach.
“Don’t just look at job boards for jobs,” says Pacific Life data scientist Alyssa Columbus. “It may be tempting to look for work on company websites or check specific job boards, but according to people who are employed in the data science industry (including me), these are among the least helpful ways to find work. Instead, you should try to contact recruiters or build up your network to break into the field.”
So how can you make those connections? Let’s take a look at some of the best options.
You shouldn’t hesitate to reach out directly to data science recruiters or working data scientists on LinkedIn, particularly if they operate in your geographical area or work for a company you’re particularly interested in.
You might, for example, simply search for “data science recruiters” on LinkedIn. If you find any who are second-degree connections, this means you know some of the same people, and you may be able to get a personal introduction. Reach out to the person (or people) you have in common and see if that’s possible.
If you don’t have any connections in common, that’s okay. You can still request a connection. Rather than sending the default “add to network” message (or directly asking for a job), try to send something personal. Keep in mind that your goal is to establish some level of relationship, not just use this person for a job. It may be better to ask for help with a job-related question rather than directly asking for a job.
You might, for example, write something like:
Hi, I’m an aspiring data scientist. I saw you live in my area; could I buy you a coffee sometime? I have a few resume questions I'd love your opinion on before I start applying for jobs.
(Don’t just copy-paste that everywhere — you’ll want to tailor your message to the specifics of the situation and to more accurately reflect your own voice.)
Note, however, that it does succinctly communicate both that you’re a data scientist looking for work and that you’re trying to bring something to the table in terms of the relationship (even if it’s just a free coffee).
“When you’re trying to open a new relationship like that, it should be a two-way street as much as you can make it,” says Alyssa.
If you can’t find the right person on LinkedIn, you can always do the same sort of thing via email. Many people, particularly recruiters, have company emails posted publicly on the company’s website or on their social accounts.
Here, just as on LinkedIn, you should keep your initial message short and to the point. And here, just as on LinkedIn, the goal is to establish a relationship. Don’t start by selling yourself or asking for a job. You should be aiming to ask a question and make a real connection.
Social media can be another great way to interact with members of the data science community and make connections. Despite all the hype, data science is still a pretty small field, and even the most influential accounts tend to be backed by down-to-earth folks who are happy to discuss data science topics and offer help and advice to anyone who asks. Twitter and Quora, in particular, are networks with a pretty active data science presence and easy, direct interaction.
If you’re not sure where to start, the answers to this Quora question will be helpful. This list of data science influencers on Twitter and this list of women in data science are also good places to find folks to follow.
As with other forms of networking, the key here is to be genuine and work on building a real connection. Don’t just reach out and immediately ask people for jobs. Engage in conversations, ask for advice, ask questions about their work, and share. Become an active member of the community and over time you’ll build real connections that could lead to opportunities.
Even if you’re not comfortable participating in social media, it’s worth setting up an account and following people whose work interests you. Twitter’s data science community shares job postings fairly frequently, and some of them are jobs you would be unlikely to find searching via job boards. You’ll find helpful career tips and articles get shared on Twitter and Quora frequently, too.
Meetups and Events
Data science and tech industry meetups and events can be a great way to connect with other people in the community, network, and find jobs. Often, hosts will even ask the audience “who’s hiring?” so that you can easily spot the folks with whom you should be trying to chat.
In fact, meetups may be one of the best places to look for data science jobs. “Rather than putting your resume in a pile with the others, attend local meetups in data science and machine learning," Edouard Harris advises. “That’s the way. Most organizations don’t actually hire very much through the CV route. They hire through back channels. The way into a person’s back channel is primitive: putting your face in front of their face repeatedly, in various settings, and smiling at them.”
Outlier.ai CTO Mike Kim agrees: “Meetups, conferences, anywhere where you can meet people in person, and actually build relationships,” he says. All are good sources of jobs and job leads.
Meetups are typically free, and many one-off events such as talks and panel discussions are either free or relatively affordable, so they don’t require much investment beyond a few hours of your time. And the value of getting your face (and ideally, a sample of your work) in front of data science employers in your area is well worth that investment; you’ll likely get more out of two hours networking at a meetup than you would out of two hours searching and applying for jobs on big job boards.
If you’re actively looking for jobs, you should try to come to meetups with something you can show to recruiters, potential employers, and anyone else who may be able to connect you with a job. There are more details about this in our article about project portfolio creation later in this guide, but here’s the short version: have a project that’s visual, accessible on your phone, and perhaps even a little interactive. Being able to whip out your phone and actually show somebody a unique data science project is going to be far more impactful than simply telling them about your skills.
Even if you’re not looking for jobs, it’s worth going to these events. Meeting people and building relationships now will make your life easier when you do want to look for jobs, and you can learn a lot from talking with more experienced data analysts and data scientists.
Remember: going to events is only worth your time if you’re putting in the effort to meet people and network. Most events try to actively facilitate this (there’s often scheduled networking or social time before or after presentations), but you still have to be willing to put yourself out there and talk to people you don’t know. This article is a good source of helpful tips for networking at events.
So where can you find meetups? If you’re in a major metropolitan area, this should be relatively easy. In major cities in the U.S., Europe, and Asia, you will likely be able to find events by simply searching for “data science” on Meetup.com. Depending on your country, there are probably also local Meetup.com equivalents; China-based data scientists, for example, can search for events on Douban. If you’re on Facebook, you can also search Facebook for data science events (just search for “data science” and then filter the results to include only events in your preferred city or location. Eventbrite also has an event search feature, although events listed there are likely to cost money.
If you’re in a less populated area and aren’t finding much related to data science, you may have to get a bit more creative. Search for related terms like “machine learning” or “big data,” and search for events tied to specific data science skills or languages like Python or R. These kinds of events can be great opportunities for networking, even if they’re not exclusively focused on a data science topic. Even general tech industry meetups can be worth your time.
If you really can’t find anything, consider starting your own data science meetup in your area. That requires a fair amount of legwork, but it’s not as difficult as you’d think. We’ve published a guide on how to do it, and although it takes time, it also puts you right at the center of a new local data science community.
If you want to cram a ton of valuable networking into a very short period of time, or build a broader network of contacts outside of your local region, you should look into attending some data-science-related conferences. Conferences present a dual career opportunity, says Olivia Group CEO Olivia Parr-Rud, because “not only can you learn but you can network with people. Sometimes that’s the best way to gain an advantage for a job.”
The downside of conferences is the cost, and it’s often significant. Ticket prices can vary a lot, but they typically cost hundreds or thousands of dollars, and there are often additional costs conference-goers need to consider, like airfare, lodging, and dining costs.
There are a few ways to get around these costs, though!
Get your employer to pay for you. This is the most common approach, and probably the easiest. If you’re working in a completely unrelated field, this may be a tough sell, but if you’re currently doing something data-related or can sell the conference as a valuable networking or learning opportunity that will help you in your current role, you may be able to get your employer to cover your costs, as many companies have budgets for this sort of professional development.
In fact, this approach to attending conferences is so common that many conferences actually offer “convince your boss” resources: stats and arguments you can show your manager to help make the case for your attendance, downloadable email request templates, etc. It’s worth taking a few minutes to poke around on the website of any conference you’re interested in to see if they offer something like that.
Be a presenter or panelist. That may seem out of reach, but don’t sell yourself short. There are lots of conferences out there, and they all need presenters. If you’ve done something cool or unique, you’ve got a decent shot at becoming a presenter, particularly at smaller or community-run conferences. This is a great way to get into conferences for free and add an attractive qualification to your resume.
Look into ticket discount options. Many conferences offer discounts designed to help defray the costs for certain types of attendees. Groups like full-time students, non-profit employees, and teachers will often be able to get cheaper tickets, and group discounts are also frequently available. You might, for example, be able to team up with some other interested Dataquest students in our Members Slack and get a group discount together.
Get a media pass. If you have any experience with writing or photography and you know of blogs or news outlets that might be interested in covering the conference, don’t hesitate to approach them and pitch the idea of you covering the conference for them as a freelancer. The worst they can say is no, and if they do agree, you can typically get a free or heavily discounted conference ticket from the conference organizers. Look for information on the conference site about “media passes” or “press passes” to see if this is a viable option.
Skip the conference, but get the networking. If you’re in the area of a conference but don’t have a ticket, you can usually still do some valuable networking simply by hanging out in locations near the conference center and/or hotel. In the morning, find the closest Starbucks; in the evening, find the closest bar or visit the hotel bar. It’s probably not worth spending the time and money to travel to another city without a conference ticket, but if a conference is coming to your city, you can get a lot of networking value without spending a dime simply by being in the public places conference-goers (your networking targets) are likely to go.
Which specific conferences will be the best use of your time and money depends on your own goals, as well as your location and your schedule. KDNuggets keeps an actively-updated list of data-science-related events and conferences you can find here, and you can also take a look at crowdsourced opinions on the best conferences to attend in the US or Europe or India via Quora.
Friends and Family
Finally, don’t forget about your personal networks. Not everyone is going to be fortunate enough to find a job in this manner, but at a minimum, you should make sure that friends and family all know that you’re actively looking for data science work, so that they can pass along any opportunities they come across.
If you don’t have family or friends in the field, you probably shouldn’t expect many opportunities to come your way via this channel, but telling friends and family about your job search is quick, easy, and can’t possibly hurt. Your competitors for data science jobs will certainly be making use of their personal networks where possible, so you should do the same.
Make Your Own Job: A High-Risk, High-Reward Approach
Pramp CEO Refael Zikavashvili suggested an alternative approach to finding data science jobs that’s also worth considering, although it has some inherent risks:
“If the candidate pro-actively sends me an email and tells me, ‘Look, I believe that you have this business challenge and this is how I would go about solving that,’ that will super impress me,” he says. “Somebody who can actually identify problems just based on public information and more than that will go the extra step and actually suggest a way to solve that, that will blow me away.”
“That person gets an instant basically interview from me,” he says. “No questions asked. I would even skip the resume at that point.”
This approach could be used in lieu of a traditional job application, Refael says, but he also recommended applicants try it with companies they’re passionate about even if that company doesn’t have an open data science job listing.
“Find a company that you really like, that you’re passionate about,” he suggests. “Analyze that company. Find a challenge that you think you can help with. Come up with a solution. That’s the best advice that I can give somebody. That’s a way to stand out.”
In theory, this approach (we previously wrote about it here) can work because it very quickly communicates three things that employers like to see in their job applicants:
- You’re passionate about their company and solving its problems.
- You have the business sense and technical skills needed to address those problems and propose a solution.
- You’re proactive and strongly self-motivated.
Needless to say, this is a highly risky strategy. It requires a lot of prep time: you’ve got to conduct extensive research on the company in question, identify a real problem, and do a little data science project to solve that problem in some way. Then you’ve got to find the right person to contact and communicate your work to them very quickly and clearly. If any part of that goes wrong, you’ll have spent a lot of time on a single job application and gotten nothing in return.
It’s an approach we would recommend considering only with companies and jobs you really want — the kind of company you’re 100% sure you’d join if you got a job offer from them.
You can also apply a lower-stakes version of this approach at companies where you’ve applied traditionally and been granted an interview—we’ll discuss this in more detail in the chapter on job interviews.
TL;DR: Where to Find Data Science Jobs
The recruiters we spoke with didn’t agree on a single best channel for finding and applying for data science jobs, but they did virtually all agree that relationship-based applications were typically more likely to get a result than “cold” applications. Some suggested conferences, some meetups, and some online networking and relationship-building, but nearly everybody felt that being “known” in some way gives applicants a real advantage.
Of course, if you’re looking for a job as soon as possible, that presents a bit of a challenge, since you can’t build a relationship or even necessarily orchestrate any personal interaction overnight. That’s why we recommend you start working on networking and relationship-building right now, no matter what stage of your career journey you’re in.
This article is part of our in-depth Data Science Career Guide.
- Introduction and Table of Contents
- Before You Apply: Considering Your Options
- How and Where to Find Data Science Jobs — You are here.
- How to Write a Data Science Resume
- How to Create a Data Science Project Portfolio
- How to Fill in Application Forms, When to Apply, and Other Considerations
- Preparing for Job Interviews in Data Science
- Assessing and Negotiating Job Offers