Overview Kaleris is seeking a passionate and pragmatic Data Scientist to join our AIML team in building the next generation of AI solutions for logistics and supply chain. You will design, implement, and validate analytical and learning-based models to power optimization, routing, and decision-making across our products.
Responsibilities Design, implement, and optimize end-to-end ML/RL workflows for dynamic decision-making and operational optimization.
Build and maintain training environments to safely evaluate model behavior prior to live deployment.
Develop robust data pipelines for ingestion, cleaning, feature engineering, and labeling; conduct EDA, hypothesis testing, and model diagnostics.
Define rewards, constraints, and safety checks; plan offline evaluations and controlled experiments to validate model 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.
Ensure responsible AI practices, model governance, and reproducible experimentation throughout the ML lifecycle.
Collaborate with product, engineering, and operations to translate business goals into measurable ML objectives, success metrics, and deployment plans.
Contribute to requirements/design/code reviews; submit major solution components for peer review prior to deployment.
Produce clear documentation, visualizations, and stakeholder-ready narratives; plan sprint work, track tasks, and report progress.
Requirements Bachelor's or Master's in a quantitative field (Computer Science, Statistics, Mathematics, Operations Research, Engineering).
2–4 years of relevant experience in data science/ML.
Solid background in statistics, data modeling, machine learning, and visualization.
Practical experience applying reinforcement learning concepts (e.g., policy learning, reward design, evaluation) to real-world problems.
Proficiency in Python and one deep learning framework (PyTorch or TensorFlow).
Strong data skills: SQL, Pandas, NumPy, Scikit-learn; visualization with Matplotlib/Seaborn/Plotly.
Software craftsmanship: Git, unit testing, code reviews, and reproducible experiments (e.g., MLflow).
Experience deploying models to cloud environments (Azure/AWS/GCP) using Docker/Kubernetes and CI/CD.
Comfortable working with global/distributed teams; effective communication, problem-solving, and professionalism under pressure.
Preferred Simulation experience (discrete-event or agent-based) and familiarity with queueing/ stochastic modeling.
Logistics/supply-chain domain exposure (terminal operations, yard management, transportation networks, inventory flows).
MLOps practices: model versioning, monitoring, and automated retraining.
Experience with optimization methods and decision intelligence in complex, noisy environments.
Why Join Kaleris? Competitive salary and comprehensive benefits.
Inclusive, diverse, and collaborative team culture.
The chance to build AI products at the frontier of decision intelligence for the supply chain.
Opportunities to grow your domain expertise while delivering measurable impact in real-world operations.
Kaleris is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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