Burns Sheehan
Lead Data Scientist

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🚀 Hiring: Data Science / Machine Learning Engineering Lead | Remote UK
📍 Remote-first UK role - Commutable to Manchester once a month
🤝 Engineering meetup once a month (Manchester, first Tuesday)
🌍 Full company meetup once a quarter
💰 Competitive salary package (£90k-£100k+ depending on experience)
I’m working with a long-established, growing technology business looking for a Data Science / Machine Learning Engineering Lead to take ownership of a key part of their platform.
This is a role for someone who sits between ML engineering, data science, and technical leadership. Someone who still enjoys getting into the detail of models and architecture but also loves building teams, mentoring people, and turning technical vision into something that gets delivered.
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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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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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.
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No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
You’ll be joining a team working on complex data problems at scale, including:
- Building and improving production machine learning models
- Owning the full ML lifecycle, from development through to deployment, monitoring, and optimisation
- Working with large-scale streaming data environments
- Helping shape technical direction and platform strategy
- Leading and developing a talented team across ML Engineering, Data Science, and Data Engineering


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They’re looking for someone with:
- Strong commercial Machine Learning experience
- Python experience
- Recent experience with Apache Spark
- Experience building and deploying ML models in production
- A track record of leading and developing engineers/data scientists
Databricks experience would be beneficial, but strong fundamentals and the ability to pick up new technologies are key.
If you’re a technically strong ML leader who wants genuine ownership over how a platform evolves, I’d love to chat.
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