Job Description

Kaleris is seeking an experienced Senior Data Scientist to lead the design and delivery of machine learning and reinforcement learning solutions across our logistics and supply chain products. You will own end-to-end modeling initiatives—from problem framing and data strategy through validation and production operations—leveraging AI tooling to drive measurable business outcomes.

Responsibilities
- Lead end-to-end ML solution design, translating business objectives into modeling plans, success metrics, and deployment strategies.
- Architect training and evaluation environments, including simulators, to safely assess model behavior prior to live deployment.
- Establish data quality standards and build reliable pipelines for ingestion, cleaning, feature engineering, labeling, and experiment tracking.
- Define rewards, constraints, and safety considerations; plan offline evaluations and controlled experiments to validate performance.
- Validate and operate production models: define acceptance criteria and test plans, continuously monitor performance/latency/drift,



investigate anomalies, and apply corrective measures in partnership with software engineering.
- Drive responsible AI practices, model governance, documentation standards, and reproducible experimentation throughout the ML lifecycle.
- Partner with product and engineering to scope initiatives, prioritize roadmaps, and integrate models into scalable services with robust observability.
- Mentor and coach data scientists; lead technical reviews, elevate code quality, and promote best practices.
- Communicate findings and recommendations to stakeholders and leadership through clear narratives and visualizations; influence decision‑making with data.

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 mode

📌 Senior Data Scientist (Valencia)
🏢 Kaleris
📍 Valencia