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Data Scientist (LLMs & Conversational AI)
About the role
We are building production conversational AI systems powered by large language models, speech AI, and real-time AI workflows.
We’re hiring two Data Scientists to join our LLM team, helping design, improve, and evaluate the systems that power our Conversational AI products in production. The focus is on defining, interrogating, and solving complex modelling and evaluation problems, and building the data and experimentation foundations that will generate systematic advances in conversational AI.
You’ll work closely with researchers, engineers, and evaluation teams across areas such as LLM behaviour and reasoning, prompt optimisation, conversational quality, and model evaluation. We’re particularly interested in people from strong quantitative backgrounds including Mathematics, Physics, Statistics, Computer Science, or related fields, who enjoy piecing together difficult problems, thinking rigorously, and working in environments driven by research and experimentation.
What you’ll do
- Design and use datasets, experiments, and evaluation methods to gain detailed and intimate knowledge of our conversational AI solutions.
- Identify opportunities to improve quality, reliability, and performance through research and experimentation.
- Statistically investigate how changes to prompts, pipelines, models, or system logic impact real-world outcomes.
- Think creatively about edge cases that might expose flaws in a system, and devise and test different ideas about how to handle those cases without harming global performance.
- Develop tests and metrics that capture the many nuances of user requirements, rather than uncritically relying on industry-standard proxies that might obscure underlying issues.
- Advance the building blocks behind LLM-powered systems, including classification, ranking, retrieval, scoring, and conversational workflows.
- Collaborate with engineering and evaluation teams to turn product problems into measurable AI problems.
- Stay up to date with the latest research and critically apply new ideas to production AI systems, separating out strong, flexible approaches from fragile hype.
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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What we’re looking for


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- Degree, MSc, or PhD in Mathematics, Physics, Statistics, Computer Science, or a similarly quantitative field.
- Strong Python skills with experience using tools such as pandas, NumPy, and scikit-learn.
- Experience working with data science, experimentation, statistical analysis, or machine learning systems.
- Curiosity around the finer details of LLMs, NLP, or conversational AI.
- Ability to think critically about model behaviour and system performance rather than simply training models.
- Sceptical mindset: you are uncomfortable with taking any assumption as given, and you examine new ideas with an open but critical mind.
- Comfortable working in fast-moving, collaborative environments with ambiguous problems.
Why join us
- Work on real-world conversational AI systems used in production.
- Solve difficult applied AI problems in a highly collaborative environment.
- Join a research-driven team working across LLMs, speech AI, and evaluation.
- Influence how AI systems are measured, improved, and deployed at scale.
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