You’re looking for your next big job, but every time you apply to a position, it’s crickets. You’ve got the background and the training. Your GitHub profile is filled out, and you’ve done your hackathons. The field has a talent demand, but if you’re making these mistakes, you may not be attracting the attention your abilities deserve. Here are 4 ways data scientists are marketing themselves incorrectly in the job market and what to do about it.
[Related Article: 7 Reasons to Start (or Update) Your Github Profile]
You’re Too Focused on Tech
Your latest, fanciest algorithm is awesome, and you have a good reason to be excited about it. Algorithms don’t sell themselves, however. If you’re too focused on building the coolest algorithm instead of how you plan to help others with your programming, you may be missing the mark in business.
Business decision-makers may or may not understand the underlying features and complexity of your algorithms. Instead, if you can successfully convey the applications of your algorithms in real-world terms, decision-makers may understand more about how your abilities could benefit the company and others.
Some things you can do: You could start a medium account (or write for ours), to communicate how you see algorithms being applied in real-world settings. Another good place to publish is LinkedIn where your potential employers are already hanging out. You can also present at conferences or even at local meetups.
You Don’t Understand Business
Data scientists don’t have to have an MBA, but some measure of domain-level expertise can help bridge the gap between theoretics and real-world applications. While you were in academics or training, the complexity of your program was how you got recognition, but in the business world, delivering real business value is key.
Business value doesn’t depend on complex programs. Business prefers to take a shorter, simpler route to value. You must understand how to get “good enough” data to move decisions forward while maintaining the integrity of your program. The better you immerse yourself in the business world, the more likely you’ll be able to make these types of connections.
Some things you can do: If you’re not ready to niche down into a field of expertise, like healthcare or finance, you should at least show an understanding of how the business moves. You might shadow connections in the business world or apply for short term internships. You can also take free courses on aspects of business like this one from edX.org or this specialization from Coursera.
You aren’t Networking
Networking has evolved from those awkward drinks to a variety of ways to meet people (introvert-friendly included). You can do a lot of networking online now through platforms like LinkedIn. Instead of reaching out and directly asking for a job, connect with those in the field whose work you admire. Try something like this:
“Hey, (Name) – I’ve been impressed by (something specific about) your work/paper/company/model/etc. and I was wondering if we could connect. I work in data science/AI/machine learning, and I’d love to keep up with what you are/your company is doing.”
Simple. Then follow up. Comment on their posts. Reach out to congratulate them on an accomplishment every so often. Be on their radar. Eventually, you might broach the subject of a job but put them as people first.
Another great networking option is to attend conferences and meetups. Open Data Science has them regularly and they’re a great way to meet people in your field, whether it’s peers and potential startup partners, or mentors and those in your dream organizations.
Some things you can do: Try to connect with two people in your field each week and nurture a conversation. Find your closest meetup and consider one of ODSC’s conferences or others.
You’re Doing What Everyone Else is Doing
What sets you apart from a sea of boot camp graduates? You learned Python and know your way around Pandas. You’ve got a filled out Github. You took a boot camp course or a MOOC of some kind. That’s great.
The glory days of data science are still here, but businesses may not understand how your skills translate to business value, so it’s up to you to do things to stand out. You might try replicating papers with your own dataset spin and writing about it on LinkedIn. You could present at local meetings or get good at building your own datasets using a Web Scraper.
Some things you can do: Develop your “boring skills.” Learn about the business itself. Develop a reputation for doing things in ways no one else is and (repeat after me) translate that to business value.
[Related Article: 7 Reasons Your Data Science Resume is Suboptimal]
Marketing Yourself the Right Way
Finding your own niche in the data science and AI field doesn’t have to happen at the domain level although that can help. Once you get a reputation for understanding the value of data science from a business perspective and you put in some time networking genuinely with people in your field, you may find your prospects looking a lot better.