Hi ,
In last Monday's email, I introduced the paradox many data scientists face: the technical skills that help you land your first job often aren't the same skills that help you advance your career.
Today, I'm diving into the first of four career-limiting mistakes that keep data scientists from earning what they're worth.
Mistake #1: Growing Technical Skills Instead of Business Savvy
"Your subject matter expertise gets you to the table... but it's your process expertise that gets you hired." - Blair Enns
Have you ever noticed that data scientists have a tendency to collect technical skills and certifications as though
they're Pokémon - much like my friend in last week's story?
I'm not judging. I used to do the same.
I think it's because, once you embark on a career built solely on technical expertise, it then becomes necessary to maintain that expertise.
I'm constantly hearing the following from data scientists looking to advance their careers:
"I'm focusing on my technical skills for now, and I'll learn the business skills once I get the promotion."
But it's a race you can never win.
With new data science and AI technologies being released every other day, there is always another skill to learn or course to complete. Eventually, you find yourself learning skills that aren't even relevant to your current role - just in case.
The eight cloud certifications the friend I mentioned in last Monday's email earned? They were across AWS, Azure and Google - to be prepared for whatever might arise.
What she was really doing was buying an insurance policy for imagined future opportunities, while ignoring the opportunities right in front of her
nose.
I get it. Growing your technical skills feels safe and you can tell yourself you're "doing something" for your career. But at the end of the day, you're just procrastinating from growing the skills that might be outside your comfort zone but that businesses actually value in data leaders.
What actually drives career advancement are skills such as:
- The ability to translate business challenges into data opportunities;
- Communication skills to influence non-technical stakeholders;
- Strategic thinking that connects data work to business outcomes; and
- Project prioritisation based on organisational impact, not
technical interest.
Ask yourself who creates the greater business value: A data scientist who can build cutting-edge models far beyond what the organisation can implement, maintain or understand, or one who can deliver practical solutions to real business problems using the technical capabilities that already exist?
Leadership knows the answer - and they promote accordingly.
To paraphrase Blair Enns, your technical skills get you to the table, but it's your business skills that build your career.
What to Do Instead?
Technical skills aren't Pokémon. You don't have to "catch 'em all".
Breaking free from the certification collection mindset isn't easy, but it's essential if you want to
grow your data science career.
Learn the technical fundamentals of data science by all means - you can't build a career without them. But then deliberately shift your focus to developing the business skills that leverage your existing technical skills.
For example,
rather than optimising your model to increase the accuracy by an additional 0.1%, spend the time translating your model results into business metrics (such as dollars and cents) instead.
If you need any additional technical skills along the way, learn those as you go - guided by business needs, not what looks good on your resume.
Watch for next week's email, where I'll cover Mistake #2: Taking Orders Instead of Taking Charge - another critical shift that separates data science leaders from those who remain stuck in junior roles.
What's one technical skill you've been thinking about learning that might not actually be necessary for your
current role? Hit reply and let me know.
Talk again soon,
Dr Genevieve Hayes.
p.s. If you missed the first email in this series, or if you prefer to
read the complete article now rather than waiting for the weekly instalments, you can access it in its entirety HERE.
