Data Scientist – Machine Learning for Personalization & Recommendations

About the role

We are looking for a Data Scientist to join a fast‑paced, product‑driven team in Switzerland. The role focuses on designing, building and productionising machine‑learning systems that power personalization, search and recommendation features for a global user base. You will work closely with product and engineering colleagues in a remote‑first environment to turn data into measurable business impact.

Key responsibilities

• Develop, implement and productionise ML models for personalization, search ranking and recommendation systems.
• Design data‑driven algorithms that improve user experience, engagement and conversion rates.
• Apply advanced statistical and data‑mining techniques to large‑scale datasets.
• Collaborate with engineering and product teams to integrate models into production and ensure scalability.
• Monitor model performance, iterate based on real‑world data and business feedback.
• Translate business problems into analytical and ML solutions aligned with key performance indicators.
• Work with cloud infrastructure and data pipelines to support scalable ML workflows.
• Contribute to A/B testing frameworks to validate model effectiveness.

Required profile

• Minimum 2 years of experience as a Data Scientist, Quantitative Analyst or similar role.
• Strong background in probability, statistics, machine learning and linear algebra.
• Bachelor’s or Master’s degree in a quantitative field such as Applied Mathematics, Computer Science, Engineering or Financial Engineering.
• Proven track record of applying ML models to real‑world business problems with measurable impact.
• Excellent communication skills and ability to explain technical concepts to non‑technical stakeholders.

Required skills

• Python programming language.
• Pandas, NumPy, scikit‑learn (or equivalent) libraries.
• SQL and relational database experience.
• Experience with cloud platforms (AWS, GCP or Microsoft Azure).
• Knowledge of end‑to‑end ML model productionisation and data pipelines.