Data Scientist Reinforcement Learning - Lisbon at Workster Jobs – Lisbon | Jobyssey

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

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