Staff Data Scientist
Job Description Staff Data Scientist Function: Data Location: Hybrid, London office Curious about what’s next? So are we. Join Compare the Market and help to make financial decision making a breeze for millions. At Compare the Market, we’re a purpose-driven business powered by tech and AI. We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers. We’ve carved a meerkat-shaped niche and we’re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you’ll fit right in. We’d love you to be part of our journey: As the Staff Data Scientist, you will deliver high‑impact AI and decisioning solutions while raising the bar for how we discover, experiment, develop and productionise ML and AI models at Compare the Market. You’ll partner closely with product, engineering and machine learning engineering to take the most important use cases from problem framing and research through to robust, measurable value in production. Set technical direction for complex initiatives, mentor other data scientists, and champion responsible, scalable practices across the model lifecycle. Some Of The Great Things You’ll Do Data Science Strategy & Delivery
- Lead mission‑critical data initiatives from discovery to deployment and continuous improvement, with clear success metrics and guardrails.
- Tackle complex, ambiguous problems through research, structured experimentation, modelling and optimisation; reduce risk through iterative hypotheses and A/B and/or multivariate tests.
- Define the technical approach and roadmaps for high‑priority use cases within a domain using a range of paradigms and frameworks (e.g. supervised, unsupervised, reinforcement learning, foundation models); align with Product & Engineering to deliver outcomes to agreed timelines and quality standards.
- Apply and contribute to best practices for experimentation, modelling and measurement (including uplift/causal methods), ensuring reproducibility, versioning and lineage.
- Review and raise the standards on critical models (e.g. feature engineering, validation, bias/leakage prevention); translate research into pragmatic, production‑ready methods.
- Partner with machine learning and AI engineers to productionise robust batch/real‑time services following best practices; embedding monitoring, drift detection, explainability and fairness standards.
- Act as a technical coach and mentor across squads –mentoring time is supported; provide design/analysis reviews and uplevel senior ICs and early‑career talent.
- Influence roadmaps by collaborating with Product, Engineering and Data leaders on priorities and trade‑offs; represent Data Science in architecture and design forums.
- Contribute to the AI/ML platform direction by shaping requirements and partnering with platform owners; drive adoption of reproducible, reliable workflows.
- Foster a culture of learning, transparency and cross‑functional collaboration; share decisions, assumptions and outcomes openly.
- Evaluate emerging research and market trends (e.g., LLMs, recommender / optimisation methods, knowledge graph, transformers) and turn promising ideas into prototypes and patterns.
- Embed responsible AI in day‑to‑day delivery—explainability, fairness and compliance—and promote continuous improvement through demos and write‑ups.
- Proven experience delivering high‑impact ML/AI solutions in complex, data‑rich environments, including production deployment and post‑launch iteration.
- Advanced hands‑on proficiency in Python and core DS/ML libraries; strong SQL and familiarity with distributed data tooling.
- Strong understanding of experimentation and statistical inference; experience designing trustworthy A/B tests and measurement frameworks.
- Strong grasp of ML system design and the model lifecycle (from discovery and experimentation through to deployment, monitoring and governance).
- Ability to lead cross‑functional technical work with multiple stakeholders and to influence without authority.
- Excellent communication and storytelling skills, able to convey complex ideas simply and align teams on decisions.
- Experience mentoring other data scientists and shaping best practices across a team.
- Background in a quantitative field (e.g., statistics, computer science, mathematics, engineering) or equivalent applied experience.