
What if I told you that some of the most famous Data Scientists in the world aren’t actually Data Scientists?
Take Cassie Kozyrkov, for example. Cassie used to be Chief Data Scientist at Google, but for the last 5 years has been working as Google’s Chief Decision Scientist. Or take Chris Dowsett, who began his career as a Statistician and worked up to becoming the Head of Decision Science at Instagram.
In 2023, most of the hype around "data-driven jobs" relates to GenAI. But lurking in the background there’s another new kid on the block, quietly making waves in in the world of Data.
Meet the Decision Scientist.
Over the last few years, more and more organisations have been employing Decision Scientists. And I’m not just talking about niche startups in Silicon Valley – I’m talking about mammoth organisations from Meta to Manchester United and everyone in between.
But what on earth do Decision Scientists actually do? And how should those of us working in traditional Data Science react to the rise of Decision Science?
Introducing Decision Science: the shy younger sibling of Data Science
According to Cassie Kozyrkov, the aforementioned Chief Decision Scientist at Google, Decision Science is the discipline of turning information into actions.
It’s the science of selecting between options.
She writes:
Decision Intelligence is an interdisciplinary field concerned with all aspects of decision-making. It combines Data Science (statistics, machine learning, AI, analytics) with the Behavioral Sciences (psychology, neuroscience, economics, and the managerial sciences).
Isn’t this just a rebranding of Data Science?
If you’re sat there thinking that this sounds suspiciously similar to Data Science, you’re not alone. That was my first reaction, too.
But the key to Cassie’s definition is that fashionable little word: "interdisciplinary." If Data Science is primarily a quantitative discipline based on applied statistics and ML, we can think of Decision Science as a discipline which includes both quantitative and qualitative methods. Its primary focus is on making decisions, and this makes it much more holistic than traditional Data Science (which often gets too preoccupied with simply answering questions with statistics or improving products with ML).
Admittedly, Decision Science doesn’t have as many job postings or HBR articles as Data Science. But as a Data Scientist, I’m convinced that the rise of Decision Science is going to have a profound impact on the Data industry, and those of us working in Data Science would be wise to take note.
Why? Because it shows that Data Science is fragmenting… and that’s a good thing.
Data Science is fragmenting… and that’s a good thing
For the first 10 years of its existence, Data Science was the cool new kid in town (if I hear one more reference to "sexiest job"…).
A Data Scientist used to be a jack-of-all-trades, doing everything from business analysis to ML engineering. We were the go-to people for solving business problems with data, equally comfortable in the boardroom and the engine room (at least in theory).
But in 2023, that’s not really the full picture anymore. In many large corporates, Data Scientists don’t present to execs anymore; they’ve got Analytics Translators to do that. They don’t build pipelines or productionise models; they’ve got Machine Learning Engineers and Data Ops teams who do that. And in some companies, Data Scientists don’t even build ML models; they’ve got Machine Learning Scientists who do that.
The trend is clear: Data Science is fragmenting.
What was once a proudly interdisciplinary field has become increasingly siloed and specialised. And, interestingly, the Data Science job description of 10 years ago (≈ "uses ML, stats, and critical thinking to solve business problems") almost seems to apply more aptly to a modern-day Decision Scientist than to a modern-day Data Scientist (at least, in large companies).
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How should Data Scientists react to the siloing of Data Science and the rise of Decision Science?
If you’re a Data Scientist or Data professional reading this, I think there are three main reflections to take away:
1. Decision Science reminds us to go beyond Stats/Math and remember the value of qualitative approaches
One of the things I like about Decision Science is that it emphasises the value of interdisciplinary approaches.
All too often, contemporary Data Science can feel more like Applied Math, narrowly obsessing over algorithms and technical details rather than keeping in mind the bigger picture. We build fantastic models, but fail to move the needle in corporate contexts because we’re not addressing the right question or aren’t managing to land our message with the right decision-makers.
Decision Science corrects this bias by reminding us that data are just tools for answering questions and solving problems, and that – to reach the optimal decisions – we need to draw on lots of fields including behavioural science and psychology.
You might never get (or want) the job title "Decision Scientist," but interdisciplinary approaches are valuable to all of us working in Data. If you’re interested in incorporating non-Math approaches, check out Cassie’s excellent article, where she introduces questions from Behavioural Economics like "How does changing the presentation of information influence choice behavior?" and questions from Psychology like "How do emotions, heuristics, and biases play into decision-making?"
2. The rise of Decision Science opens up interesting career opportunities for Data professionals
When I was getting into Data in 2019, I read countless Reddit posts complaining about the state of Data Science. Back then, Data Scientists didn’t have a clear career path, and the jack-of-all-trades label meant that many Data Scientists were getting bogged down in tasks like Data Engineering or Visualisation, which weren’t really viewed as part of the core Data Science "canon."
The rise of specialisms like Decision Science is a welcome antidote to this confusion. By distinguishing between Data Science and Decision Science, companies are better able to delineate between the responsibilities of different data folk, and Data professionals have more options in terms of where they want to specialise.
Don’t get me wrong – there’ll always be a need for generalists in Data Science, so the generalist Data Science roles won’t disappear. But in big organisations, we might see more Decision Scientists, and on a personal level, I think this is an excellent thing.
Why? Because if you’re more interested in the math/engineering side of things, then you have the option to focus more on MLOps or Data Engineering. Or, if your primary interests are in problem-solving, then Decision Science might be a better fit for you, and you won’t have to get as bogged down in ML Engineering or Data Governance.
(OK, I lied slightly – we will never fully escape Data Governance! But the point is we have more options).
3. AI will turn all of us into Decision Scientists
The final thing I want to say is that the rise of AI is making Decision Science skills all the more important.
As my professor at Oxford always used to say, AI is great at answering questions it is asked, not posing questions never before voiced.
Decision Science orients our attention to asking the right questions, and in the age of Prompt Engineering and AutoML, I can hardly think of a more valuable skill.
One more thing –
I’ve started a free newsletter called AI in Five where I share 5 bullet points each week on the latest AI news, coding tips and career stories for Data Scientists/Analysts. There’s no hype, no "data is the new oil" rubbish and no tweets (or should I say ‘x-es’ now?) from Elon – just practical tips and insights to help you develop in your career.
Subscribe here if that sounds up your street! Thanks for reading.