We’re hiring a Data Science Lead in London to build and lead our Predictive Growth Optimisation team – pioneering ML models that power our user acquisition strategy, predict lifetime value, and optimise our $25M+ annual marketing spend across channels.

This role owns the strategy, development, and continuous improvement of Flo’s pLTV system - a mission-critical model reused across UA, AdTech, personalisation, and financial forecasting. That is the core of the role. Alongside it, you'll stand up a Marketing Mix Modeling (MMM) capability to measure cross-channel effectiveness and inform budget allocation, and develop the algorithms to drive real-time UA campaign management. You'll lead a team building production systems that directly impact our growth trajectory, staying as hands-on as you choose.

What you’ll do

• Lead & develop a team of 6+ ML and Backend engineers - hiring, mentoring, and setting technical direction
• Own pLTV strategy - architect and evolve our core predictive lifetime value models that inform millions in UA decisions
• Stand up MMM - build our Marketing Mix Modeling capability: adstock and saturation modelling, channel contribution, and budget allocation, calibrated against our incrementality experiments
• Power real-time campaign management - develop the algorithms that optimise our UA campaigns across channels in real time
• Build production ML systems - from real-time prediction services handling millions of daily predictions to MMM models
• Drive cross-functional impact - partner with Growth, Product, and Finance to translate business problems into ML solutions
• Shape technical architecture - guide MLOps infrastructure, monitoring, and rapid iteration cycles
• Stay as hands-on as you choose - modeling, architecture decisions, technical problem-solving; your call how deep you go.

What you bring

Technical Leadership

• 7+ years applied ML experience building and deploying models in production
• 4+ years managing technical teams (ML engineers, data scientists, or similar)
• Expert knowledge of ML fundamentals: supervised/unsupervised learning, time series; strong grounding in causal inference
• Experience with modern ML frameworks (TensorFlow, scikit-learn, CatBoost)

Growth & Product Experience

• Experience with growth analytics, attribution modeling, or marketing effectiveness
• Understanding of user acquisition funnels and retention optimization
• Comfortable translating business requirements into technical roadmaps
• Strong communication skills - can explain complex models to executive stakeholders

Production ML Systems

• Experience deploying ML models at scale
• Knowledge of MLOps practices: model versioning, monitoring, automated retraining
• Understanding of data engineering fundamentals and cloud platforms

Nice to have

• Hands-on experience building Marketing Mix Models end to end, Bayesian or regression based
• Background in consumer tech, mobile apps, or health tech
• Knowledge of privacy-preserving ML techniques and A/B testing methodology