Engineering Manager (Data Science Team)

Join Flo, a leading women's health app, as an Engineering Manager for our Data Science Team. In this role, you will build and lead our Predictive Growth Optimisation team, develop our pLTV system, and establish a Marketing Mix Modeling capability. You will lead a team of 6+ ML and Backend engineers, drive cross-functional impact, shape technical architecture, and have the option to stay hands-on in modeling and technical problem-solving. Enjoy benefits such as participation in Flo's Employee Share Ownership Plan, generous parental leave, access to learning resources, and a flexible workplace.

• Lead and develop a team of 6+ ML and Backend engineers, hiring, mentoring, and setting technical direction.
• Own the strategy, development, and continuous improvement of Flo’s pLTV system, architecting and evolving core predictive lifetime value models.
• Stand up a Marketing Mix Modeling (MMM) capability to measure cross-channel effectiveness and inform budget allocation, and develop algorithms for real-time UA campaign management.
• 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 optimization
• 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