Senior/Lead Data Scientist

Join our team as a Senior/Lead Data Scientist and play a key role in bringing our new product vision to life. You will work end-to-end across metric design, data and modeling, experimentation, and product integration. Your contributions will help turn real customer signals into timely actions and measurable outcomes. You will also establish experimentation and measurement foundations, productionize and scale workflows, and collaborate with various teams to ensure the success of our product. Enjoy a range of benefits including flexible working, private medical insurance, and an annual learning & development budget.

• Contribuer activement à la réalisation de la vision du nouveau produit en travaillant sur la conception des métriques, les données et la modélisation, l'expérimentation et l'intégration du produit.
• Établir des fondations d'expérimentation et de mesure en concevant comment tester, apprendre et prouver l'impact, en intégrant des pratiques statistiques solides.
• Productionner et mettre à l'échelle de manière réfléchie en collaborant avec l'équipe d'ingénierie pour expédier des flux de travail de données et de modèles durables.
• Track record, typically 4+ years, in applied data science for product or marketing in consumer or SaaS
• Hands-on ML skills: feature engineering, propensity or uplift modeling, model evaluation, monitoring
• Practical data engineering instincts: event schemas, batch jobs, orchestration, data quality guardrails
• Strong Python and SQL with the ability to move from notebooks to production code
• Clear communication that translates complexity into actionable narratives for non-technical audiences
• Continuous learning mindset: you stay current with generative AI advances, prototype with new models and frameworks, evaluate them critically, and translate useful innovations into practical product improvements. You share learnings and raise the bar for the team
• Experience in designing and implementing model/metric endpoints as part of a platform ecosystem with clear contracts and SLOs (e.g., FastAPI/Flask, OpenAPI), deploying via AWS Lambda/API Gateway or containers
• Experience with dbt, Airflow, Looker or Metabase, AWS services such as S3 and Lambda
• Personalization: propensity/uplift modeling, bandits, causal inference

Libraries: scikit-learn, XGBoost, LightGBM, CatBoost, CausalML, Keras, DoWhy

• Graph modeling
• LLM applications for agentic solutions
• Bias for action and ownership in ambiguous, fast-moving environments