Data Scientist Reinforcement Learning

Descrição do trabalho

Workster is partnering with a global mobility-tech leader to find a talented Data Scientist (Reinforcement Learning) to join their cutting-edge tech hub in Lisbon. In this role, you will design and implement advanced statistical models and real-time decision engines that directly power millions of dynamic pricing decisions per day.

• You'll work at the intersection of pricing strategy, statistical modeling, and scalable machine learning systems—owning the full lifecycle from analytical ideation to production deployment.
• Your Role
• Design advanced statistical models: Prototype regression-based pricing models (linear, GLM, Gaussian Process, mixed-effects) using a Bayesian framework
• Apply scalable inference methods: Use SVI, MCMC, and other techniques to process large-scale, high-volume, or streaming datasets
• Extend bandit algorithms: Improve multi-armed bandits with contextual features, richer priors, and uncertainty quantification
• Develop clean, reproducible pipelines: Build end-to-end pipelines for feature engineering, label generation, and automated data quality checks using Airflow or Dagster
• Package and deploy models: Use tools like FastAPI, Docker, or Kubeflow to serve modular ML services in production
• Monitor and detect anomalies: Set up performance dashboards and Bayesian control charts to catch data drift, overfitting, and anomalies in real-time
• Experiment and evaluate: Design A/B tests, multivariate experiments, or apply causal inference when randomization isn't feasible
• Drive business impact: Collaborate with product managers and analysts to turn complex questions into measurable insights
• Share knowledge: Mentor peers, publish internal technical insights, and lead hands-on workshops on Bayesian workflows
• Your Qualifications
• Strong statistical foundation: 5+ years applying regression, hierarchical models, or state-space methods in real-world settings
• Bayesian modeling expertise: Hands-on with PyMC, Stan, NumPyro, TFP, or similar libraries; confident building custom priors and likelihoods
• Experience in variational inference: Familiar with SVI, black-box VI, or advanced MCMC techniques at scale
• Software engineering mindset: Solid Python skills with familiarity in type hints, testing, and CI/CD best practices
• Cloud and orchestration fluency: Experience with AWS/GCP/Azure and tools like Docker, Kubernetes, or workflow schedulers
• Business communication skills: Capable of explaining uncertainty, lift, and risk clearly to both technical and executive audiences
• Continuous learner: You actively follow the latest developments in probabilistic ML and enjoy sharing knowledge with others
• The Offer
• Generous time off: 28 vacation days per year, your birthday off, and one volunteer day
• Work-life balance: Hybrid work setup, flexible hours, and no dress code
• Health & wellness: Private health insurance to support your well-being
• Perks & discounts: Coverflex benefits platform and discounts on transport, travel, and more
• Learning & development: Access to tech talks, external conferences, and training tailored to your growth