Lead Bayesian RL Scientist - Pricing & Personalization

SIXT; Lisboa; Presencial; EUR 70 000 - 110 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Private health insurance

Meal allowance

SIXT exclusive rates

Resumo da oferta

SIXT Lisbon is hiring a Senior Data Scientist to build probabilistic models powering pricing and offers. You will own end-to-end modelling—from question framing to live deployment—working with a global tech hub and a culture of experimentation.

Responsibilities include designing two-tower personalization, applying Bayesian methods (SVI/MCMC), and deploying via modern stacks (FastAPI, Docker, Kubeflow) with real-time monitoring and robust CI/CD practices.

Qualificações

• 5+ years of applying regression, hierarchical models, or state-space methods in production.
• Strong Bayesian toolkit and production-grade Python coding.
• Experience with cloud deployments and container orchestration.

Responsabilidades

• Architect and prototype advanced regression models with Bayesian approach for pricing at scale.
• Design, productionise personalization and recommendation systems with modern architectures.
• Apply SVI, MCMC and importance sampling for uncertainty-aware decisions.
• Build reproducible pipelines for feature engineering and data validation.
• Deploy models via FastAPI, Docker/K8s; monitor with Bayesian charts and dashboards.
• Design and analyse A/B/multivariate tests; quantify value of information.
• Collaborate with product, revenue, and customer-experience teams; mentor peers.

Conhecimentos

Bayesian statistics

Regression modeling

Experiment design

Stakeholder communication

Production ML

Cloud platforms

CI/CD

Python

A/B testing

Ferramentas

PyMC

Stan

NumPyro

TFP

Airflow

Docker

Kubernetes

FastAPI

Dagster

Descrição da oferta de emprego

SIXT Lisbon is hiring a Senior Data Scientist to build probabilistic models powering pricing and offers. You will own end-to-end modelling—from question framing to live deployment—working with a global tech hub and a culture of experimentation.

Responsibilities include designing two-tower personalization, applying Bayesian methods (SVI/MCMC), and deploying via modern stacks (FastAPI, Docker, Kubeflow) with real-time monitoring and robust CI/CD practices.