Hi ,
Over the past few weeks, I've shared three critical mistakes that can stall a data science career: the "skills collection trap", becoming an order-taker instead of a strategic partner, and letting projects die in the "POC
graveyard".
Today, I'm tackling the fourth and final career-limiting mistake - one that can negate all your hard work even when you've avoided the first three pitfalls.
Mistake #4: Letting Your Wins Die
In Silence
"Numbers have an important story to tell. They rely on you to give them a clear and convincing voice." - Stephen Few
But what if you do finally succeed and deliver a data science solution that solves a real
business problem? Surely that should be enough to guarantee career success?
If only it were that easy.
Data scientists frequently make the mistake of believing that their work speaks for itself, when in reality nothing could be further from the truth.
This silence happens for several reasons.
Many data scientists are uncomfortable with communicating their wins, through presentations and written reports, preferring to leave it for their manager to do.
Others become bored once the problem is solved, and immediately chase the next challenge instead of optimising the value of their success and demonstrating its full business impact.
And some simply lack the business vocabulary to translate their technical achievements into terms executives understand.
The cost? Your wins remain invisible to the decision makers who control your career trajectory, while colleagues who are able to communicate the business benefits of their less impressive results command the promotions that should rightfully be yours.
I've seen this play out time and again
in job interviews. When asked to give an example of previous work, candidates who simply say "I built a classification model" rarely ever advance. But a candidate who tells me how their classification model increased customer retention by 12% and generated $1.5m in additional revenue? They've almost certainly got the job.
Same work. Completely different outcome.
Of the four mistakes, this is the greatest of them all because it can easily negate months of brilliant work.
To paraphrase the old proverb, if a data scientist saves an organisation $500k and nobody is around to quantify it, did the saving even happen?
What to Do Instead?
Management guru Peter Drucker famously said, "what gets measured gets managed".
If you're serious about managing the growth of your career, then you need to start measuring the value of your work and communicating the impact you're creating.
This isn't bragging or "someone else's" job. Translating your technical accomplishments into the language leadership understands is the crucial last step that closes the loop between
technical solution and business value.
As a data scientist, measurement should come naturally to you. Before starting your next piece of work:
- Identify the specific business metrics your solution is expected to influence;
- Establish a clear
baseline of metrics before implementation;
- Design a simple comparison study to quantify the before-and-after impact; and
- Translate these technical measurements into business outcomes like revenue, cost savings or time saved.
For example, instead of saying "I built a customer churn model with 86%
accuracy", this strategy puts you in the position to say "My customer retention solution identified at-risk accounts worth $3.2M in annual revenue, allowing us to retain 74% of them through targeted interventions."
Once you quantify your impact in business terms, craft a narrative that explains to stakeholders why this should matter to them by answering these three essential
questions:
- What? (i.e. what is your solution and its measurable results?)
- So What? (i.e. why do these results matter to the business?)
- Now What? (i.e. what should the business do now to make use of these results?)
The data scientists who advance fastest aren't necessarily those who create the most sophisticated models - they're the ones who effectively communicate the business value of the work they do, no matter how simple that work may seem.
So What?
Over the past four weeks, we've explored four critical mistakes that can stall a data science career. Let's bring it all together.
Technical skills are an essential foundation of any data science career - but they aren't the only thing necessary to excel. And like everything in business, investment
in technical skills follows the law of diminishing returns.
The most successful data scientists aren't necessarily those with the deepest technical knowledge, but rather those who can translate whatever skills they possess into business solutions that matter. These are the data scientists whose careers and compensation consistently outpace their technically brilliant peers.
Whatever technical skills you currently possess, they're likely already sufficient for your next career leap. What's missing isn't more algorithms - it's strategic application.
Instead of signing up for yet another certification, focus on these three career accelerators
instead:
- Develop a deep understanding of your organisation and the challenges your stakeholders are actually trying to solve;
- Deliver simple, implementable solutions to today's pressing problems rather than theoretical answers to tomorrow's imagined challenges;
- Quantify and communicate the business impact of your work in
language decision-makers understand and value.
This strategic shift is what transforms your perception from "just another data person" to "indispensable business partner" - the kind executives turn to first and compensate accordingly.
Are you ready to stop
collecting skills and start accelerating your impact?
If so, but you're not sure exactly how to make this transition, I can help. My Data Science Impact Accelerator program is specifically designed for technically-skilled data scientists who want to maximise their business impact and earnings potential.
In just 12 weeks, I'll guide you through developing the exact business skills covered in this article - from uncovering high-value opportunities to quantifying and communicating your impact.
To learn more about how you can transform your data science expertise into real business value and financial rewards, reply to this email
with the word "IMPACT".
Talk again soon,
Dr Genevieve Hayes.
p.s. If you missed any of the previous emails in this series, or if you would like to
read the complete article, you can access it in its entirety HERE.
