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Data Scientist Early Hire, Full Model Ownership, B2C SaaS

Jobgether

Remote
Remote Mid 🇬🇧 English
Python SQL scikit-learn PyTorch TensorFlow

Description du poste

About the role

This early‑stage Data Scientist position is based in Switzerland and operates in a remote‑first environment. You will own the entire machine‑learning lifecycle, turning data into predictive models that directly influence product strategy and revenue growth for a B2C SaaS business.

Key responsibilities

  • Design, build, validate, deploy, and monitor ML models for churn prediction, CLV forecasting, propensity, uplift, and recommendation systems.
  • Own end‑to‑end model development: feature engineering, training, evaluation, deployment, monitoring, and continuous optimization.
  • Run and analyze experiments (A/B tests, causal inference) to measure business impact.
  • Establish modelling standards, experimentation frameworks, and best practices across the organisation.
  • Collaborate with data engineering to productionise models via scalable pipelines and feature stores.
  • Translate analytical results into clear business recommendations for product, growth, finance, and leadership.
  • Ensure privacy‑conscious modelling and compliance with data governance.

Required profile

  • 3+ years of experience building, deploying, and maintaining production ML models.
  • Strong proficiency in Python and SQL.
  • Hands‑on experience with modern ML frameworks such as scikit‑learn, PyTorch, or TensorFlow.
  • Solid grounding in statistics, experimentation methodology, hypothesis testing, and causal inference.
  • Proven track record with predictive use cases like churn prediction or customer‑lifetime‑value forecasting.

Required skills

  • Python
  • SQL
  • scikit‑learn
  • PyTorch
  • TensorFlow

What we offer

  • Full ownership of high‑impact ML models.
  • Broad exposure to product, growth, finance, and leadership teams.
  • Autonomous, fast‑paced, remote‑first work environment.
  • Opportunity to shape data‑science practices from the ground up.

Questions fréquentes

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