Gleeson Recruitment Group
Data Scientist

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Founding Data Scientist / Machine Learning Scientist
Fully remote role - Odd trip to London office
Subriction - Service based business
We're working with an exciting business that is building its data science capability in-house for the first time, and they're looking for a Founding Data Scientist to shape the function from the ground up.
This is a genuinely hands-on role where you'll have ownership of the full data science lifecycle - from defining the modelling approach and building predictive models through to creating scalable, repeatable and production-ready solutions.
The initial focus will be on customer churn and complaint propensity, alongside forecasting and other predictive modelling opportunities. You'll be responsible for building the models, documenting the methodology and establishing the overall approach that can be embedded and scaled across the business.
What we're looking for
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
We're particularly interested in Data Scientists who have built churn propensity models before, ideally within a subscription-based or service-led business where customer retention is a key commercial challenge.
You'll need to be comfortable working independently and owning the end-to-end process without relying heavily on Data Engineering or a separate ML Engineering function. This means being able to take models from concept through to productionisation and build approaches that are scalable, repeatable and robust.
Key skills
- Strong commercial Data Science / Machine Learning experience
- Proven experience building customer churn / propensity models
- Strong Python and SQL
- Experience taking ML models into production
- Ability to design and document modelling approaches from scratch
- Strong understanding of forecasting and predictive analytics
- Experience working with subscription, recurring revenue or field/service-based businesses is highly desirable
- Microsoft technology stack experience
- Experience using LLMs to analyse classified/unstructured data would be a bonus


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This is a rare opportunity to join at the beginning of the journey and define how Data Science is done within the business, rather than simply inheriting an existing framework.
If you've built churn models before and want genuine ownership, autonomy and the opportunity to build something from the ground up, we'd love to hear from you.
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