Engineering Manager (Data Science Team)

Responsibilities

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.

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.

• 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.

Benefits

• Participation in Flo’s success: We firmly believe every employee contributes to the company’s growth and its future success. All employees are eligible to participate in Flo’s Employee Share Ownership Plan (ESOP) and be awarded equity to participate in the long‑term value creation of the business.
• Family benefits: We know having a baby can be a big transition for the family, so we’re proud to offer 1 month fully paid paternity leave to be there and bond with your baby and 6 months of fully paid maternity leave, with a $5000 bonus on your return to work to help you settle into this new chapter of your life.
• Resources you need to thrive: At Flo you will have access to internal and external learning resources and tools to challenge your knowledge and push yourself further every day. We support career progression from within through personal development plans and dedicated Learning & Development budget to help you thrive.
• Holiday and sick leave: It’s important to take time off to recharge and spend time with family and friends. We offer 25 days (28 for vice presidents and above) paid holiday in addition to the local public holidays and 30 days fully paid sick leave per year.
• Flexible workplace: We want to provide a setup that gives our employees the flexibility they need, while also getting important face to face time with the team. With this in mind, we encourage teams to spend 2 days/week in the office. Our popular Workation policy also allows you to work from anywhere for up to 2 months a year.

Qualifications

• 7+ years applied ML experience building and deploying models in production.
• Expert knowledge of ML fundamentals: supervised/unsupervised learning, time series; strong grounding in causal inference.
• Strong communication skills - can explain complex models to executive stakeholders.
• Comfortable translating business requirements into technical roadmaps.
• Knowledge of MLOps practices: model versioning, monitoring, automated retraining.
• Experience with growth analytics, attribution modeling, or marketing effectiveness.
• Experience deploying ML models at scale.
• Experience with modern ML frameworks (TensorFlow, scikit‑learn, CatBoost).
• 4+ years managing technical teams (ML engineers, data scientists, or similar).
• Understanding of user acquisition funnels and retention optimisation.
• Understanding of data engineering fundamentals and cloud platforms.
• Background in consumer tech, mobile apps, or health tech.
• Hands‑on experience building Marketing Mix Models end to end, Bayesian or regression based.
• Knowledge of privacy‑preserving ML techniques and A/B testing methodology.