Trying to Change Careers or Get Your Start in Data Science?
If you’re someone who is looking to make a move to data science, there are some ways that you can polish your approach to get noticed during your job search.
Assuming that you've built up the skills required for the job see if you're able to leverage some of these tips:
Optimize your resume (as best you can) for the job you WANT not the jobs you’ve HAD.
Try to gain experience at your current job (if you’re a career changer), or work on your own data science projects at home. (continuous learning is a big plus).
Develop a killer elevator pitch.
Optimizing your resume for the job you want:
Describe your projects in a way that shows you’re results-focused.
The points you’re going to want to demonstrate on your resume need to both:
Demonstrate that you understand general corporate culture, and showcase your collaborative, result achieving, problem solving and self-managing competencies.
Show that you have the technical chops as a data scientist.
The first bullet takes a lot of thought - it is really easy to list job duties, it’s another thing to reword them effectively to highlight your true strengths and demonstrate how what you've done has improved the business. Your bullet points should be full of action verbs and results, even if you need to stretch yourself mentally to identify these.
Did you automate a process that saved hours of time manually doing a task? That time saved is business value.
Demonstrating that you've worked cross-functionally or presented results to the business are again, things that are desirable for the new job you want (data scientist).
It is helpful to read job descriptions and see what companies are looking for, you'll find consistent themes. If you look closely, you'll see there are a lot of skills listed that aren't necessarily technical. Make sure you shine when speaking to those softer skills. But of course, these softer skills need to be demonstrated in a way that still demonstrates an action and result. Do not just put a "soft skills" section on your resume and list a bunch of words with no context.
"Show you have the technical chops as a data scientist". This is pretty straight-forward. Try to use the verbiage from the actual job description for the job you're applying to. You might want to sound fancy, but “empirical bayesian 3-stage hierarchical model” probably isn’t on the job description. Having this specifically listed on your resume isn’t going to help you pass ATS (the applicant tracking system), and the person in human resources who doesn’t have a data science background is not going to know whether that is relevant or not. Again, looking at multiple job descriptions and trying to gauge what type of language to use on your resume is helpful.
Gain experience at your current job or work on a project:
If you currently have a job, do you have access to SQL? Does your company have a data warehouse or database? Can you file a ticket with the service desk to get SQL? Can you then play with data to make your own project?
You could even go a step further and bring data from the database into R or Python. Maybe you make a nice decision tree that answers a business questions then wonderfully and concisely place your results of your project on your resume.
Try to automate a task that’s repeatable that you do on a regular cadence. That’s next level resume content. You’re increasing efficiency in this scenario.
If you’ve done data science projects on your own to round out your resume, make sure those bullets are full of action verbs and results, action verbs and results. I almost want to say it a third time.
SQL Lite is open source, R is open source, Python is open source, there is tons of free data out there. The world can really be your oyster, but you’ll need to market these go-getter skills effectively.
Develop a killer elevator pitch:
A strong, well-targeted resume might open the door, but you need to keep that door open and keep the conversation going once the door has been opened. The resume does nothing more than open the door, that’s it.
Getting your resume into the right hands can sometimes be difficult. Leveraging LinkedIn effectively can help bridge that gap. How do we begin the conversation if you’re reaching out to someone on LinkedIn to ask about opportunities?
Important note: When cold reaching out to people on LinkedIn, this should be after you have visited the company website, found a job that you’re interested in and (pretty much) qualified for, and then you reach out to a relevant person with a well-targeted message.
It is impossible to be well-targeted if you are reaching out to someone who works at a company that doesn’t have any positions available. Because you didn’t read a job description. So you wouldn’t be able to infer the needs of the business. Data Science is a large field, with many specializations, a blanket approach will not work.
Back to the pitch. You’re results-focused, you’re innovative, and you view things from the business’ perspective.
I'd suggest starting with something conversational, this will help if the person you're messaging is already being inundated with requests. A comment about a post they made recently makes your connection come across as more authentic.
Why you’re messaging: you’re interested in the open position, and you’re trying to get your resume to the correct person.
Then mention a number of things concisely that are specifically mentioned on the job description. Basically saying “hi, look at me, I’m a fit.”
Let them know that you’d really appreciate it if they’d simply forward you to the correct person (hopefully the person you’re messaging is the correct person, but there is also a chance it’s not the right person, so don’t assume).
Close strong. You’re here to add value for the company, not to talk about your needs; imply you’re aware that you’re here to talk about how you can fit the needs of the business.
Hi [name],
I enjoyed your recent post on [topic] and I look forward to reading more of your posts.
I noticed [company] is hiring for [position title], and I’m hoping I can get my resume in the right hands. I have an MS in Statistics, plus 7 years of real-world experience building models. I’m a wiz at SQL, modeling in R, and I have exposure to Python.
I’d appreciate the opportunity to speak with the appropriate person about the open position, and share how I’ve delivered insights and added value for companies through the use of statistical methods.
Thanks, Kristen
Now you may have a very different background from me. However, you can talk about the education that you do have (concisely), the exposure that you do have to building models, about your technical chops, and that you want to deliver value.
I hope that you’ll be able to use some of these suggestions. And I wish you a successful a rewarding career in data science. If you have additional suggestions for trying to make a change to data science, I’d love to hear your thoughts! The next article I post will be covering how to write crisp content for your resume that makes an impact, that article is here.
Up-Level Your Data Science Resume - Getting Past ATS
This series is going to dive into the tip of the iceberg on how to create an effective resume that gets calls. When I surveyed my email list, the top three things that people were concerned about regarding their resumes were:
- Being able to get past ATS (Applicant Tracking System)
- Writing strong impactful bullet points instead of listing “job duties”
- How to position yourself when you haven’t had a Data Science job previously
This article is the first part of a three-part series that will cover the above mentioned topics. Today we’re going to cover getting past ATS.
If you’re not familiar with ATS, it stands for Applicant Tracking System. If you’re applying directly on a website for a position, and the company is medium to large, it’s very likely that your resume will be subject to ATS before:
1. Your resume lands in the inbox of HR
2. You receive an automated email that looks like this:
It’s hard to speak for all ATS systems, because there are many of them. Just check out the number of ATS systems that indeed.com integrates with https://www.indeed.com/hire/ats-integration.
So how do you make sure you have a good chance of getting past ATS?
1. Make it highly likely that your resume is readable by ATS
2. Make it keyword rich, since ATS is looking for keywords specific to the job
Being readable by ATS:
There has been a movement lately to create these gorgeously designed resumes. You’ll see people “Tableau-ize” their resume (ie — creating a resume using Tableau), include logos, or include charts that are subjective graphs of their level of knowledge in certain skill sets. An example of one of these charts looks like this:
ATS is not going to know what to do with those dots, just as it wouldn’t know what to do with a logo, your picture, or a table; do not use them. To test if your resume is going to be parsed well by ATS, try copying the document and pasting it in word. Is it readable? Or is there a bunch of other stuff? You can also try saving it as plain text and see what it looks like.
As data-loving story tellers, I understand the desire to want to show that you’re able to use visualizations to create an aesthetically appealing resume. And if you’re applying through your network, and not on a company website, maybe you’d consider these styles. I’m not going to assume I know your network and what they’re looking for. And of course, you can have multiple copies of your resume that you choose to use for specific situations.
What is parsable:
I’ve seen a number of blog posts in the data world saying things to the tune of “no one wants to see one of those boring old resumes.” However, those boring resumes are likely to score higher in ATS, because the information is parsable. And you can create an aesthetically pleasing, classic resume.
Some older ATS systems will only parse .doc or .docx formats, others will be able to parse .pdf, but not all elements of the .pdf will be readable if you try to use the fancy image types mentioned above.
Making your resume rich with keywords:
This comes in 2 forms:
1. Making sure that the skills mentioned in these job descriptions are specifically called out on your resume using the wording from the JD.
2. Reducing the amount of “fluff” content on your resume. If your bullets are concise, the ratio of keywords to fluff will be higher and will help you score better.
For point 1, I specifically mention my skills at the top of my resume:
I also make a point to specifically mention these programs and skills where applicable in the bullet points in my resume. If a job description calls for logistic regression, I would add logistic regression specifically to my resume. If the JD calls for just “regression,” I’ll leave this listed as regression on my resume. You get the idea.
It's also important to note that more than just technical skills matter when reading a job description. Companies are looking for employees who can also:
- communicate with the business
- work cross-functionally
- explain results at the appropriate level for the audience that is receiving the information.
If you’re applying for a management position, you’re going to be scored on keywords that are relevant to qualities that are expected of a manager. The job description is the right place to start to see what types of qualities they’re looking for. I’ll have highlighted specific examples in my resume course I’m launching soon.
For point 2, you want to make your bullet points as concise as possible. Typically starting with a verb, mentioning the action, and the result. This will help you get that ratio of “keywords:everything” as high as possible.
In my next article in this series I'm sharing tips on how to position yourself for a job change. That article is here.