AI use disclosure: 0 AI used in this particular post.
Perhaps about once a month someone messages me on LinkedIn or the Hive Slack community, asking how a data scientist/analyst/engineer can use their career to help animals. This is something I’ve spent nearly 5 years thinking about! I originally trained as a data science after my PhD because I wanted to build skills to make the world a better place. These are my reflections on what I’ve learned, and conversations I’ve had with others.
Definitions
- Data professional: umbrella term for data scientists, data analysts, data engineers, statisticians, MLOps people, analytics engineers blah blah….
- I’m going to use the term “animal protection” to describe both animal protection non-profits and alternative protein companies. Most alternative protein companies don’t consider themselves to be part of the animal movement (or wouldn’t admit it publicly), but my assumption is that the reason you the reader would consider going into alternative proteins is because you want to help animals. So I’m just going to lump them all in together.
Paradoxically, while there is demand for data science in the animal protection movement, there is little demand for data professionals in animal protection organisations.
The bad news
Simply put, most orgs cannot afford a data professional. Not only that, they don’t have enough data, analytics or machine learning (ML) use cases to need even part-time data person. This includes both non-profits as well as alternative protein companies (who are usually small startups). I typically see 1-3 data roles a year advertised across the whole movement. There are probably less than 15 data scientists in paid employment in the entire movement.
How can organisations need data science but not data scientists?
Across all organisations, animal or no, for profit or non-profit, there is growing need for data skills. However, this doesn’t necessarily mean there is a growing need for dedicated experts like data scientists. Generally, it’s a growing need for everyone to have data fluency. Here are some ideas off the top of my head about how “non data people” might leverage data, machine learning and AI:
Data science skills and use cases for animal protection organisations
More generally:
- Any role that has to use spreadsheets at some point would benefit from data skills. If you have spreadsheets, you have data. Some data skills make you better at using those spreadsheets, and if you know R or python you can take that data out of spreadsheets and do much more with it (or more likely, direct AI to do it).
- Literally everyone needs to learn to use generative AI, because it is already impacting basically all jobs.
- AI models can do data analysis, but often make questionable analytical decisions that only a data person would catch.
More specifically:
- Marketers might upskill in AB testing
- Researchers or Analysts might learn to scrape websites.
- Researchers would also likely benefit from statistical training (which data professionals usually have) to do better “guesstimating”: estimating the size of a market, or the potential impact a program could have.
- If you monitor your organisation’s socials or comms, then social media analytics is important to understand what’s doing well and what isn’t.
- Product/project managers, as well as executives at medium to larger organisations would often benefit from dashboards and automated reports, but might only need a few.
- Fundraisers might pull together enough data on donors to do some cluster analysis to understand what types of people donate to the organisation.
- Website/app developers might add simple AI features into apps and websites
- Any organisation that wants to get serious about evaluating the impact of its programs and campaigns would benefit from “causal inference”, a discipline from economics that is increasingly being brought into data science.
I’m a data professional that wants to help animals. What are my options?
There are broadly 3 key ways a data professional can directly participate in the animal protection movement and one indirect way:
- Doing a non-data job where you can use data skills
- Fractional roles
- Consulting
- Earn to Give (spoiler: this is the main one I recommend)
Use your data skills inside a non-data job
As outlined above, plenty of roles within animal protection/alt proteins would be enhanced with some data skills. You could be one of those people! Whereas outside the movement you might be a data analyst working in the marketing department, in the movement, be a marketer that can do data analytics.
It may feel strange or even sad to train as a data scientist, only to not “be” a data scientist. It may even make you feel like the training was pointless. But I would advise placing your identity as an animal advocate first, and focus on your commitment to helping animals. Ask yourself not “how can I help animals as a data professional?” but “How can I help animals with my data skills?”. Or perhaps you could create a new identity of yourself as a “hybrid data scientist/marketer” or “data-driven fundraiser”.
Fractional roles
Some people work part-time at one organisation and part-time at a different one, splitting their work week between 2 organisations. For example, in early 2024 I worked 3 days a week at Peak AI as a data science consultant (non-animal) and 2 days a week at Bryant Research as a data driven researcher (an animal protection research org). Then, I split my week 50:50 between Bryant and being the Director of Data and AI at Greener by Default (I’ve since handed off the role but remain a part time data consultant for them).
Fractional roles split it down even further: You might work at 3,4 even 5 organisations. Each one only needs you (and can probably only afford you) for 1 day a week, or 1 week a month, or any other arrangement. This is likely to be more possible in the animal protection space than many other areas of the economy because organisations are all allies, rather than direct competitors. You could never work for Adidas and Nike at the same time, because they’re direct competitors. But you can simultaneously work for two organisations that both want to end factory farming. It would even have the indirect benefit of increasing potential for collaborations. At Bryant Research, some of our Bryads also work in academic labs or campaigning orgs, and we all benefit from being closer to each other (they’re not data scientists but the logic is the same).
It seems to be common in the animal protection movement to have multiple part time roles, so orgs are likely to be open to a fractional role arrangement if it benefits them. If you have your eye on a few organisations, I advise speaking with a few of them about this option and making them aware of it if they are not.
Consulting
Another option is for the animal protection movement to have consultants and consulting firms that concentrate expertise and then do contract work for various organisations.
Currently, when it comes to specifically data, there are a few consultants, but no consulting firms. I think the movement could always use strong data consultants, but don’t think there’s enough demand for a consulting firm specifically for data. However, a consultancy network where data consultants share work and help each other might be useful (I give this a mild but not trivial chance of success).
I’d probably not call yourself a data consultant (or similar) though. I would aim to become a Monitoring and Evaluation (M&E, sometimes called MEL) consultant or a research consultant that specialises in data-heavy projects. The main analysis needs of orgs are data-heavy research projects, and using data to measure the impact of their campaign for funders. The hard truth for data people here is that there are already research consultants and MEL consultants that have domain knowledge you won’t have, and probably enough data skills to do a reasonable job. You’ll have to put the time in to build your skills in MEL or research.
I won’t repeat the standard generic pros and cons of being a consultant. The disadvantage compared to fractional roles is that consultants are external to the organisation and so do not know all the organisational context and culture. This can make them slightly less effective at delivering projects if those projects are not well planned and the consultant is not good at discovery and scoping (i.e. figuring out the real problem to solve, not naively solving the problem the client puts in front of them). You also don’t always feel like part of a team which can be a bit lonely.
It is worth noting that while consultants typically earn good money, this is not the case in animal protection. The animal protection movement is terribly underfunded. Expect to earn significantly less than if you were a consultant for for-profit companies (as in, like 20-40% less!). That said, if you want to earn lots of money, you probably wouldn’t be in this space in the first place.
Earning to give
This is an indirect way to help animals, but honestly, it’s the main one that I would recommend if you’re a data professional wanting to help animals. If you work in data, you can earn very good money in the for-profit sector.
So I advise that you do that, and then donate 10-30% of your income to animal orgs. If you are a high-paid data engineer or machine learning engineer, your donations might be enough to pay someone in the movement’s entire annual salary. The animal movement is one of the least well-funded social justice movements, so donations are often needed far more than skills.
This is probably not be a satisfying answer for many people: donating doesn’t feel the same as contributing directly. But similar to what I said above, always keep in mind that the animals must come before our own egos, and if this is the best way you can have an impact for animals, then you should feel very good about yourself for doing it. I personally have huge respect for people who donate significantly to the movement.
Find more about Earning to Give here.
What about AI consulting and generative-AI roles?
I intentionally left this out of earlier versions of this post but people keep asking me about it, so I’ll address it here. Some people feel like a data scientist joining the movement as a generative AI consultant is a natural fit. I don’t think it is.
I definitely think the animal movement needs more people with AI skills, and if you’re a data person thinking about retraining as a generative AI person, I think that’s a great idea.
However, I want to emphasise that I strongly believe that data people have little to no advantage when it comes to retraining in generative AI. AI before ChatGPT and AI after ChatGPT are technically similar tech, but in practice they are fundamentally different. If you’ve built AI solutions for a decade, I don’t think you’re any better placed to be a generative AI consultant than someone who’s been a marketer for 10 years or someone who’s worked in HR for 10 years. In fact, I personally think the people best placed to move into generative AI consulting are operations people and management consultants (I plan to write about this).
So while I would recommend people take this path, I wouldn’t say this is a particularly promising path specifically for data people. Be aware you’ll be starting from the same point as everyone else.
Conclusion
Animal protection could benefit a lot from data science skills, but lacks the funding and scale to make hiring dedicated data professional feasible. If data professionals want to make an impact for animals whilst also making a living, they might consider non-data science roles that use data science skills, fractional roles if they can get them, or staying in the for profit sector and donating.
Acknowledgements
Thanks to Antonieta Pais Nunes Lopes for inspiring this post. Also thanks to Kyle Behrend and Sam Tucker for valuable insights and comments.
As a data scientist who frequently wonders about this question, thank you for this post, it was a really interesting read!
But I'm a bit confused about your final section on "generative AI" roles, and the claim that data people have no special advantage here. What sort of roles do you have in mind exactly? If you're talking only about prompt engineering, or just about the skill of being able to use a product like Claude code/cowork well, then I can see where you're coming from, though I think I still weakly disagree. For example, if you have an understanding of how these models work under the hood then you are probably better placed to understand which tasks it will be good at and which it might struggle with (this includes really basic stuff, like knowledge cutoffs in training data, and the fact it won't necessarily remember what you asked it about the day before without a memory feature bolted on, and therefore won't improve day by day etc). You are also probably in a better position to understand things like which tasks will require higher reasoning, and which won't (which very often doesn't line up with their perceived human difficulty).
But that aside, I don't think applying generative AI to practical problems is just about prompt engineering or becoming a claude cowork super-user. If you are a business that wants to create an automated workflow where you run a certain prompt over a certain dataset at scale, on the basis of certain triggers, then you are doing something that looks a lot like data engineering (and you are going to need to write code). And if you're hitting context window limits, you'll have to manage context, by building some kind of agent orchestration/scaffolding. That's maybe closer to being a brand new skill, but it still requires strong coding ability, combined with the ability to evaluate the performance of a software tool empirically, which is a specific combination of skillsets that data scientists should already have.
It's true that with AI coding assistants, people are writing less and less code by hand. But you still need strong coding ability to use AI coding assistants effectively. Software engineering has not yet been fully automated. There's a possible future where that changes soon, and maybe that's the future informing your advice in that final section? But my personal feeling would be that in that world, most knowledge jobs probably follow shortly after anyway (if not before), so trying to plan your career development around this possible future seems challenging!