BD #13 - Make Your CV Stand Out by Focusing on Impact
Most CVs waste too much space listing technology, optimise for impact delivered instead
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When it comes to CVs, many people in technology and data get it very wrong. I've had the (mis?)fortune of reading probably well over 2000 CVs in the last few years for the many roles I've hired into across data science, data engineering, BI, even DevOps and software engineers. No one class of techie is any worse than the other from my experience - we all make these same mistakes, over and over again.
I'd like to discuss the biggest failing of most CVs, then walk through what I think should be done differently to get better results.
Long lists of technology
Many of us aren't really taught how to write a good CV. I only really had some very basic training in secondary school, by teachers that didn't even know my job existed (as it didn't back then). Yet this is one of those things we all do and becomes essential to getting the roles we really want.
Now I'm not shaming anyone here - I made this same mistake when I started out (and I'll share how I first came to realise and beat later). The fact is, for any given role technologists spend far too much of their valuable CV real estate simply listing technologies and tools they've used.
It's not uncommon to see examples along the lines of:
"Built machine learning models for churn prediction using XGBoost, SQL, Python, and Spark."
or something like:
"Worked as a data engineer using Data Factory, Azure Analysis Services, Databricks, and Azure Data Lake"
Now this might seem OK in isolation, but you work your way through ever longer CVs, this pattern gets repeated over and over.
Essentially all your CV is telling me is the job titles you had and some of the tools you used. Note there's nothing in either of the above statements about how good you are with any of those tools!
Did you use Data Factory every day, for advanced and difficult use cases, or did you just run some existing pipelines on a schedule? Are you an expert in the use of XGBoost and other models or was the approach just inherited from a former colleague?
A word of warning
Now I hope you see the problem I'm trying to point out - that long lists of technologies aren't that differentiating or insightful to most hiring managers. Before we get into the advice though I want to interject with a quick word of warning!
Many of you will be painfully aware of applicant tracking systems (ATS) and the software filtering of CVs based on tech specs for the role. These are intended to limit the load on hiring teams when they get flooded by applicants for a role - meaning they don't have to actually read every CV they get but can sift through the bulk based on some algorithm or simple filters.
Before you take any of my advice to heart please be aware that for some roles (especially at larger organisations) you'll need to play the game to get your CV past these. I'd advise speaking to recruiters that understand how these work before stripping the tech lists out, to make sure you're not causing a bigger issue for yourself by following my advice!
So what to do to stand out?
Actually Andrew Jones of Data Science Infinity posted this the day after I wrote this issue and I had to include it here because of how close it matches what I'm saying:
Andrew's point is aimed at the other side - interviewers - but the same can be said for applicants too. So much space is spent talking about you qualifications and old roles, or lists of technologies that only give generic information. It is important to know what you've studied to a point but I'm far more interested in what you've done with that knowledge. I have four degrees, including a doctorate, and my whole education section is less than a quarter of the page.
CVs really stand out when they can link what they've done to the impact it delivered or the value it created. The above examples become
"Used XGBoost, SQL, Python, and Spark to increase churn prediction performance by 23%, allowing our customer success team to start a new targeted outreach campaign reducing overall churn rates across the business."
"Using Data Factory, Azure Analysis Services, Databricks, and Azure Data Lake I built end-to-end pipelines to automate the processing of external data sources, eliminating seven daily, manual ETL workflows saving the finance team at least 15 hours of effort a week while improving data quality and testability."
See what's happening here? The first set of quotes in the section above leads to generic questions about what you used the tools for. The second set of questions guides me to ask about the use case, shows you know and understand the impact you had. They're especially interesting if I'm looking for someone to help with my churn prediction or finance ETL pipelines!
Tying in numbers is great - but it's often difficult to do, especially if it's for a role you had a long time ago. I urge you to start quantitatively keeping track of the things you do and writing about the impact they had when you're selling yourself.
If you're early career it's understood that there will be little here or that you might be so far removed from the end use case that this stuff isn't clear. Just try to break down what you've done - whether that's number of support tickets cleared, tests refactored, or documentation written - numerical, impact focused writing will make you stand out when you're next looking for jobs.
This is the best advice I got when I started going for competitive roles. I shared my CV with a friend who'd been a COO at a large company and he tore it to bits - he crossed out all the "hard worker", "excellent problem solver", and "engaged learner" stuff I'd written and just commented "this is a waste of my time reading it, get rid". He taught me that employers just want to see the impact, you're trying to de-risk yourself by convincing them you can solve their problems. And once I'd seen it I could never unsee it.
All the best,Adam
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