This job is no longer available
This job expired on 31/08/2026. It no longer accepts applications.
Data Scientist – Machine Learning for Personalization & Recommendations
Jobgether
Job description
About the role
We are looking for a Data Scientist to join a fast‑paced, product‑driven team in France. You will design, develop and productionize machine‑learning systems that power personalization, search ranking and recommendation features for a global user base. The role is highly collaborative, remote‑first and focused on delivering measurable business impact.
Key responsibilities
- Develop, implement and productionize ML models that improve personalization, search ranking and recommendation systems.
- Design data‑driven algorithms to enhance user experience and boost engagement and conversion rates.
- Apply advanced statistical and data‑mining techniques to extract insights from large‑scale datasets.
- Collaborate with engineering and product teams to integrate models into production, ensuring scalability and reliability.
- Monitor model performance, iterate based on real‑world data and business feedback.
- Translate business problems into analytical and ML solutions with clear KPI impact.
- 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.
- Bachelor’s or Master’s degree in Applied Mathematics, Computer Science, Engineering, Financial Engineering or a related quantitative field.
- Strong foundation in probability, statistics, machine learning and linear algebra.
- 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 audiences.
Required skills
- Python
- Pandas, NumPy, scikit‑learn (or similar libraries)
- SQL
- Experience with cloud platforms (AWS, GCP or Microsoft Azure)
- Productionizing ML models and end‑to‑end data‑science pipelines
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