Data Scientist – Early Hire, Full Model Ownership, B2C SaaS

OnHires

Data Scientist driving business impact by building and deploying ML models in a remote B2C SaaS company. Collaborating closely with cross-functional teams to shape experimentation and machine learning efforts.

Posted 6/19/2026full-timeRemote • 🇺🇦 UkraineMid-LevelSeniorWebsite

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

machine learningpredictive modelingfeature engineeringmodel developmentmodel evaluationmodel deploymentmodel monitoringPythonSQLstatistics

Soft Skills

communicationcuriosityautonomyproblem framingnarrative buildingcollaborationadaptabilityanalytical thinkingattention to detailleadership

Tools & Technologies

scikit-learnPyTorchTensorFlowA/B testingfeature storedata pipelinesdata-use policiesprivacy-aware engineeringproduction settingexperiment design

Industry Keywords

churn predictionLTV forecastingpropensity modelinguplift modelingcausal inferenceAI-powered productsdata complianceuser-level dataimpact measurementexperimentation framework

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

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
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

Requirements

What you’ll need

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

Benefits

Comp & perks

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