Data Science Lead in London

Flo is the world’s #1 health app on a mission to build a better future for female health. Backed by a $200M investment led by General Atlantic, we became the first product of our kind to reach a $1B valuation in 2024. With 6M paid subscribers and the highest-rated experience in the App Store’s health category, we’ve spent 10 years earning trust at scale. Now, we’re building the next generation of digital health – AI-powered, privacy-first, clinically backed – to help our users know their body better.

We are hiring a Data Science Lead in London to build and lead our Predictive Growth Optimization 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, personalization, and financial forecasting. You’ll balance hands-on technical leadership with people management, building production systems that directly impact our growth trajectory.

What you’ll do:

• Lead & develop a team of 4+ 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
• Build production ML systems - from MMM algorithms to real-time forecasting models handling millions of daily predictions
• 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 hands-on - contribute to modeling, architecture decisions, and technical problem-solving as needed

What you bring:

• 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, causal inference
• Experience with modern ML frameworks (PyTorch, TensorFlow, scikit-learn, CatBoost)
• Experience with growth analytics, attribution modeling, or marketing effectiveness
• Understanding of user acquisition funnels and retention optimization
• Strong communication skills - can explain complex models to executive stakeholders
• Experience deploying ML models at scale (millions+ predictions/day)
• Knowledge of MLOps practices: model versioning, monitoring, automated retraining
• Understanding of data engineering fundamentals and cloud platforms

Nice to have:

• Experience with Marketing Mix Modeling, attribution, or ad tech
• Background in consumer tech, mobile apps, or health tech
• Knowledge of privacy-preserving ML techniques and A/B testing methodology

How we work:

We’re a mission-led, product-driven team. We move fast, stay focused and take ownership – from brief to build to impact. Debate is encouraged. Decisions are shared. We care about craft, ship with purpose, and always raise the bar. You’ll be working with people who take their work seriously, not themselves. It takes commitment, resilience, and the drive to keep going when things get tough. Because better health outcomes are worth it.

What you’ll get:

• Competitive salary and annual reviews
• Opportunity to participate in Flo’s performance incentive scheme
• Paid holiday, sick leave, and female health leave
• Enhanced parental leave and pay for maternity, paternity, same-sex and adoptive parents
• Accelerated professional growth through world-changing work and learning support
• Flexible office + home working, up to 2 months a year working abroad
• 5-week fully paid sabbatical at 5-year Floversary
• Flo Premium for friends & family, plus more health, pension and wellbeing perks

Diversity, equity and inclusion:

Our strength is in our differences. At Flo, hiring is based on merit, skill and what you bring to the role – nothing else. We’re proud to be an equal opportunity employer, and we welcome applicants from all backgrounds, communities and identities.