At a Glance
- Tasks: Lead a team to develop ML models that drive user acquisition and optimise marketing spend.
- Company: Join Flo, the world’s #1 health & fitness app with a mission for female health.
- Benefits: Competitive salary, performance incentives, paid leave, and a 5-week sabbatical.
- Other info: Diverse and inclusive workplace with opportunities for professional growth.
- Why this job: Make a real impact in digital health while leading innovative projects in a dynamic environment.
- Qualifications: 7+ years in ML, team management experience, and strong communication skills.
The predicted salary is between 60000 - 80000 £ per year.
500M+ downloads. 80M+ monthly users. A decade of building – and we’re still accelerating. Flo is the world’s #1 health & fitness app worldwide 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 – and we’re not slowing down. With 7M 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.
The job 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.
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 optimisation
- 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
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 We support impact with meaningful reward. Here’s what that looks like:
- 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
- In‑person collaboration and work in a hybrid model, with 3 days per week spent in the office
- 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.
Data Science Lead employer: Flohealth
Flo is an exceptional employer, offering a dynamic and mission-driven work environment where innovation thrives. With a strong focus on employee growth, competitive salaries, and generous benefits including enhanced parental leave and a fully paid sabbatical, we empower our team to make a meaningful impact in female health. Our collaborative culture encourages open debate and shared decision-making, ensuring that every voice is heard as we work together towards better health outcomes.
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Lead
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Flohealth!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Science Lead at Flohealth.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Flohealth.
✨Apply Directly through Our Website
When you find a suitable opening like Data Science Lead at Flohealth, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Science Lead
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Flohealth, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Flohealth. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Flohealth
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Flohealth!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.