Data Science Manager | Data & AI Consultancy | £100,000 DOE + Bonus + Benefits | London/Hybrid (3 days a week in office)
We are excited to be working with a rapidly scaling data science consultancy that is hiring multiple Data Science Managers to support its continued growth across financial services and private equity engagements.
The Company
Our client is a leading data science and engineering consultancy, specialising in delivering applied AI and data science solutions that create measurable commercial value for some of the world’s largest private equity funds and their portfolio companies. PE-backed and with a global footprint spanning the US, UK, and India, they have built a high-performing team of several hundred experts and a client base that keeps coming back
The Role
You’ll own the full lifecycle of data science engagements, from shaping ambiguous problems through to deploying production-grade models that embed into real operational workflows.
This isn’t a research or experimentation role – it’s delivery-focused, commercially grounded, and built for someone who thrives at the intersection of rigorous data science and client impact. You’ll work across organisations at every stage of data maturity, set the technical standard internally, and develop the next generation of data scientists within the team.
The Candidate
Key attributes of a suitable Data Science Manager include:
- Deep Applied Data Science Expertise – you bring 5+ years of hands-on experience delivering end-to-end machine learning and statistical solutions in production environments.
- Commercial Problem Solver – you’re confident translating ambiguous business challenges into hypothesis-driven data science solutions, and equally comfortable presenting findings and influencing decision-making at the senior client level.
- Technical Leader – you set the bar for code quality, reproducibility, and best practice, and take pride in mentoring and developing others. You understand that the measure of success is client outcomes, not model sophistication.
- Awareness of Generative AI – some exposure to LLMs, RAG pipelines, or agentic workflows is a plus, though strong data science fundamentals will always take priority.
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