Data Scientist
Remote | Full-time | Reports to: Head of Data
We design, develop, build, and analyze. We deliver impactful digital products and services — SaaS products, AI-powered solutions, web and mobile applications. As pioneers in the digital services space, we’re a team that knows how to innovate while having fun. Remote-first, but connection and collaboration are at our core.
You will partner closely with Product, Growth, Engineering, and Finance, and help lay the foundations of how experimentation and machine learning work here. This is a hands-on, pragmatic role on a forming team — with broad scope and direct influence on the roadmap.
- Build, validate, and ship predictive models that drive the business: churn prediction, LTV forecasting, propensity and uplift modelling, and recommendation
- Own end-to-end ML workflows: feature engineering, model development, evaluation, deployment, and monitoring
- Monitor models in production and retrain or adjust them as the product and user base evolve
- Explore where AI/ML creates real product value as FutureMedia expands into AI-powered products
- Design and analyse experiments (A/B tests, uplift, causal inference), bringing rigour to how we measure impact and reduce variance
- Help shape the experimentation framework and modelling standards as foundations for the wider team
- Handle user-level data responsibly: privacy-aware feature engineering, avoiding leakage of sensitive attributes, and compliance with data-use policies
- Partner with Data Engineers to productionise models with reliable feature pipelines and, where useful, a feature store
- Translate model output into clear, actionable recommendations for Product, Growth, and leadership — tying work back to company goals
- 3+ years building and deploying machine learning models in a production setting
- Strong Python and SQL, with solid command of the modern ML stack (scikit-learn, plus PyTorch or TensorFlow where relevant)
- Sound grounding in statistics and experiment design: significance, causal inference, and uplift or propensity modelling
- Hands-on experience with predictive use cases: churn, LTV, propensity, or recommendation
- Comfort owning a model end to end — from problem framing to production and measurement, not just notebooks
- The ability to turn complex analysis into a clear narrative and a recommendation a non-technical stakeholder can act on
- Curiosity and autonomy — comfortable in a fast-moving environment where the roadmap evolves quickly
- Prior experience at a B2C SaaS, subscription, or marketplace business, with first-hand knowledge of funnels, churn, and LTV
- Experience with MLOps tooling, feature stores, or real-time inference pipelines
- Familiarity with product analytics tools (Amplitude, Mixpanel, Segment)
- Experience building an experimentation platform or ML foundations from scratch in a scale-up
- Exposure to recommendation systems, NLP, or generative AI in a product context