Hi ,
During the COVID lockdowns, a data scientist I know set out to land her first data leadership role.
Stuck home with nothing better to do, she spent her weekends and evenings
boosting her already impressive technical skills.
By the end of the lockdowns she had passed:
- 8 cloud certification exams;
- the TensorFlow Developer Certificate exam; and
- the notoriously hard OSCP (Cybersecurity) exam.
When the job was advertised, she just knew the promotion was hers for the taking. After all, how could the selection panel not be impressed with her skills?
She blitzed the technical assessment - scoring far higher than any of her peers - and was certain the interview went fine.
Guess who got the job?
Spoiler alert: Not her.
In fact, the feedback she received was "too technical".
Instead, the job
went to someone who could barely code, but had been with the business for years and knew exactly what stakeholders wanted.
Welcome to the paradox of data science careers where the technical skills that opened the door won't help you climb the ladder.
To understand
this paradox, we need to look at how data scientists are trained.
What Data Science Education Really Prepares You For
Data science degrees and bootcamps have one primary goal - to help
students land their first job. This means focusing on the technical fundamentals, with business skills taking a back seat.
But in doing so, this gives students the false idea that technical excellence is what data science is all about.
Technical skills are just half
of data science, with business skills comprising the rest.
And in the absence of a mentor to explain this reality, it can lead to four common career limiting mistakes.
Left uncorrected, these mistakes can leave you stuck building basic dashboards while others get to
tackle the most exciting projects, or worse, prevent you from getting the promotion you deserve.
Over the next four Mondays, I'll be explaining each of these four career-limiting mistakes and exactly how to fix them to transform your career impact and earnings potential.
These four mistakes are:
- Growing Technical Skills Instead of Business Savvy
- Taking Orders Instead of Taking Charge
- Building Shiny POCs That Never Make It To Production
- Letting Your Wins Die In Silence
In next week's email,
I'll dive into the first mistake - the tendency for data scientists to collect technical skills and certifications like they're Pokémon, and why this approach often backfires when it comes to career advancement.
Until then, I'd love to hear if you've experienced something similar in your career. Have you ever found that technical excellence wasn't enough to get ahead? Reply to
this email and let me know.
Talk again soon,
Dr Genevieve Hayes.
p.s. This email forms part of the larger article. If you want to continue reading
now, instead of waiting for the weekly instalments, you can access it in its entirety HERE.
