Senior Data Scientist

TL;DR

Senior Data Scientist (Machine Learning/Reinforcement Learning): Lead end-to-end ML and reinforcement learning solution design and delivery for logistics and supply chain products with an accent on training/evaluation environments, data quality pipelines, and responsible AI governance. Focus on validating and operating production models with monitoring, drift/anomaly investigation, and corrective actions while mentoring data scientists and partnering with product and engineering to integrate models into scalable services.

Location: Valencia

Company

hirify.global builds logistics and supply chain products that apply AI and decision intelligence.

What you will do

• Translate business objectives into ML solution plans, success metrics, and deployment strategies.
• Architect training and evaluation environments (including simulators) to assess model behavior before live deployment.
• Define data quality standards and build pipelines for ingestion, cleaning, feature engineering, labeling, and experiment tracking.
• Design rewards/constraints and run offline evaluations and controlled experiments to validate performance.
• Validate and operate production models by defining acceptance criteria, testing plans, and monitoring performance/latency/drift.
• Partner with product and engineering to integrate models into scalable services with robust observability; mentor and lead technical reviews.

Requirements

• Master’s in a quantitative field (Computer Science, Statistics, Mathematics, Operations Research, Engineering) or equivalent experience; PhD is a plus.
• 6+ years of professional experience in data science/ML, including delivering models to production and measuring business impact.
• Deep knowledge of statistics, data modeling, machine learning, and visualization, including practical reinforcement learning experience for real-world decision-making.
• Strong proficiency in Python and a deep learning framework (PyTorch or TensorFlow), plus solid SQL and data wrangling skills.
• Experience with experiment design, hypothesis testing, and model diagnostics, with the ability to build decision-focused visualizations.
• MLOps competence (containers, CI/CD, cloud deployment on Azure/AWS/GCP) and experience operating production ML systems with monitoring, alerting, and incident response.

Culture & Benefits

• Competitive salary and comprehensive benefits.
• Inclusive, diverse, and collaborative team culture.
• Opportunity to lead AI initiatives in decision intelligence for the supply chain.
• Significant impact on real-world operations with room to grow domain expertise and technical scope.

Hiring process

• Interviews focused on ML/production experience, experiment design, and collaboration across functions.
• Technical evaluation of modeling, MLOps, and production monitoring/incident response practices.