Data Scientist (Early Hire, Full Model Ownership, B2C SaaS | Remote EU/UA)
Remote (EU/Ukraine) | Full-time (B2B contract) | Reports to: Head of Data
About the Role
OnHires is hiring a Data Scientist on behalf of our client — a remote-first B2C SaaS company with a subscription-based product, currently building its data function from the ground up. (The client operates under NDA at this stage; we'll share full details during the process.)
We're looking for a Data Scientist who turns models into measurable product and revenue impact. As an early hire on a forming data team, reporting to the Head of Data, you'll own modelling end to end: framing the problem, building and validating the model, shipping it to production, and proving it moved a metric.
You'll 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 with broad scope and direct influence on the roadmap.
What You'll Do
Modelling & ML
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 the company expands into AI-powered products
Experimentation & Causal Inference
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
Cross-functional Impact
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
What We're Looking For (Must-Have)
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
Nice to Have
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
What We Offer
Fully remote within the EU or Ukraine
B2B contract
22 days of paid time off plus public holidays
Flexible working hours within core EU/Eastern European business hours
A rare chance to build a data function from scratch, with broad ownership and direct impact on the product roadmap
About the Role
OnHires is hiring a Data Scientist on behalf of our client — a remote-first B2C SaaS company with a subscription-based product, currently building its data function from the ground up. (The client operates under NDA at this stage; we'll share full details during the process.)
We're looking for a Data Scientist who turns models into measurable product and revenue impact. As an early hire on a forming data team, reporting to the Head of Data, you'll own modelling end to end: framing the problem, building and validating the model, shipping it to production, and proving it moved a metric.
You'll 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 with broad scope and direct influence on the roadmap.
What You'll Do
Modelling & ML
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 the company expands into AI-powered products
Experimentation & Causal Inference
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
Cross-functional Impact
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
What We're Looking For (Must-Have)
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
Nice to Have
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
What We Offer
Fully remote within the EU or Ukraine
B2B contract
22 days of paid time off plus public holidays
Flexible working hours within core EU/Eastern European business hours
A rare chance to build a data function from scratch, with broad ownership and direct impact on the product roadmap