How I Created My Data Science Dream Job


Before we begin: Next month, I'm teaching 3-5 data scientists my complete process for creating your own high-value data science opportunities in the Data Science Impact Sprint - a 4-week, 1-on-1 coaching program that will boost your strategic influence and help position you for career advancement. Scroll down to learn more...

They say you should start the job you want before you have it.

Back in 2015, I wanted to be a data science manager.

The problem? Data science was new. Data science managers were virtually unheard of in Melbourne, Australia. And even if a role were advertised, there was no guarantee it would be mine.

So, I took the only option that was left to me. I started the job where I was.

At the time, I was managing a successful BI and Actuarial team.

However, I could see that data science was the future of the business. The question wasn't whether our organisation needed data science capabilities - it was whether my team would build them or another team would.

Instead of waiting for someone to create a data science "dream team" in the future, I created a business case for why my team should start doing data science right now.

I started thinking like a strategic data scientist should. This wasn't theoretical - I actually implemented the process I now teach:

1. Understand the Business Context

I mapped the key players, business priorities and decisions. The organisation was developing a 5-year plan at that time, so I made sure I knew every detail.

2. Identify High-Value Areas for Business Improvement that Data Science Can Address

Rather than treating data science as a "toy", I sought to identify high-impact business problems only data science could solve. Before writing a single line of code, I identified over 5 specific areas where data science could create measurable business value.

3. Create a Business-Centric Project Blueprint

I created a comprehensive plan that outlined my ideas, capturing everything the business decision-makers needed to know - from expected outcomes, to resource requirements and a timeline.

4. Pitch the Blueprint to Management Using the Language of Business

I pitched that plan to the executive team, focusing on business impact, rather than technical details. I spoke their language, not data science jargon, making the strategic value abundantly clear.

Six months later, my dreams came true. My team officially became a Data Science and Actuarial team, and I was its inaugural manager.

The role was never advertised anywhere. I got it because I'd already been doing the job.

Talk again soon,

Dr Genevieve Hayes.

p.s. Today the doors open for the Data Science Impact Sprint - a 4-week coaching program where I'll work with you 1-on-1 to develop your own ready-to-pitch project proposal.

You'll learn to map your business context, identify strategic opportunities, create a compelling blueprint, and pitch it with confidence - building your strategic influence and helping to position you for career advancement.

Only 3-5 spots are available for this pilot program, with sessions starting in July-August 2025.

If you're ready to start creating your own opportunities instead of waiting for them, reply with the word "SPRINT" and I'll send you the details.

Doors close at 9am on Saturday 2nd August Melbourne, Australia Time (7pm Friday 1st August US EDT) or when all the places fill.

First published: July 20, 2025

Data Science Impact Algorithm

Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.

Read more from Data Science Impact Algorithm

How do you extract trustworthy insights from data you know has been deliberately manipulated? It's a challenge most data scientists never face. We're used to cleaning messy data, but deliberately manipulated data? That's completely next level. Yet if you're working with social media data, manipulation is the reality you're dealing with. Tim O'Hearn, a reformed social media hacker who generated millions of followers through bot manipulation, recently shared with me the harsh reality: "During...

What can ethical data scientists learn from a social media hacker? More than you might think. Tim O'Hearn is a software engineer who spent years circumventing anti-botting measures and gaining millions of followers for clients - experiences he's chronicled in the recently published Framed: A Villain’s Perspective on Social Media. Not exactly the typical guest you'd expect on a data science podcast. Here's the thing... Understanding how systems can be exploited makes you better at building...

Years of delivering "perfectly" on complex data science assignments got me nowhere. One simple solution I proposed myself got me promoted... The last time I received a grade of less than an A was when I was 15 and forced to do P.E. Since then, I went on to complete 4 degrees - all with perfect GPAs. I'm not saying this to brag, but rather to make a point. I was the poster-child for academic success. What it all came down to was learning how to play the game. Put in the hours of hard work....