Top Tech Jobs & Startup Jobs in London

Lead product-focused analytics and experimentation to uncover growth opportunities, define KPIs, analyze user behavior and retention, quantify feature impact, and communicate data-driven recommendations to cross-functional stakeholders.

Lead and scale Preply's data products function: define vision, build ML/LLM and data platform products, drive experimentation, measure business impact, hire and coach teams, and partner across Product, Engineering, and Data to deliver scalable, product-first data capabilities.

Lead development and deployment of multimodal neural search and ranking models for personalized learning. Conduct research, prototype and productionize deep learning NLP/IR solutions, collaborate cross-functionally, mentor junior scientists, and establish ML best practices.

Lead backend architecture and cross-functional projects, build scalable Python/Django services and integrations, mentor engineers, improve performance and observability, and drive data-informed product decisions.

Lead technical strategy and architecture for Learning Teams, drive end-to-end strategic initiatives, evolve core services for scalability and reliability, improve engineering practices and developer experience, mentor senior engineers, and make build-vs-buy and tooling decisions.

Lead design and implementation of Preply's ML platform: experiment tracking, artifact management, scalable GPU training and inference, CI/CD for ML, observability, drift detection, and LLM/RAG capabilities; mentor engineers and align platform with product and cost goals.

Lead FP&A, corporate finance, procurement, and Finance PMO; partner with CFO and execs on financial strategy, forecasting, investor readiness, public-company preparation, and scalable finance systems while building high-performing teams.

Own availability and performance of mission-critical services, improve scalability and observability, build platform tooling, run on-call rotations, practice blameless incident response, collaborate with product teams, and follow FinOps best practices.

Partner with Payments Operations to analyze and optimize pay-ins, payouts, and fraud. Build statistical and ML models, design experiments, monitor transaction health, improve risk rules, and deliver insights to shape product and monetization strategy.

Lead design and evolution of core analytical data models, define analytics engineering standards, build and optimize ETL/ELT pipelines, own the BI/semantic layer (Looker/LookML), drive data quality and governance, partner with cross-functional teams, and mentor analytics engineering across the company to enable reliable, scalable decision-making and experimentation.