Data Scientist at Kalderos

Valencia, Valencian Community, Spain

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Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Logistics, Supply ChainIndustries

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

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

Skills

Key technologies and capabilities for this role

Machine LearningReinforcement LearningData PipelinesFeature EngineeringEDAStatisticsData ModelingVisualizationModel ValidationHypothesis TestingModel MonitoringRL WorkflowsPolicy LearningReward Design

Questions & Answers

Common questions about this position

What is the salary and benefits like for this Data Scientist role?

The position offers a competitive salary and comprehensive benefits.

Is this Data Scientist position remote or does it require office work?

This information is not specified in the job description.

What skills are required for the Data Scientist role?

Required skills include a Bachelor’s or Master’s in a quantitative field, 2–4 years of relevant experience in data science/ML, proficiency in Python and one deep learning framework (PyTorch or TensorFlow), strong data skills with SQL, Pandas, NumPy, Scikit-learn, and experience deploying models to cloud environments.

What is the company culture like at Kaleris?

Kaleris offers an inclusive environment and comfortable working with global/distributed teams.

What makes a strong candidate for this Data Scientist position?

Strong candidates will have practical experience with reinforcement learning, simulation experience, logistics/supply-chain domain knowledge, and MLOps practices, in addition to meeting the core requirements.

Kalderos

Optimizes drug discount management platform

About Kalderos

Kalderos provides a platform designed to improve the management of drug discounts in the healthcare sector. This platform helps healthcare providers and pharmaceutical manufacturers accurately manage discount claims and avoid duplicate discounts. By using data analytics and machine learning, Kalderos simplifies the complicated process of drug discount management, making it more efficient and clear. Clients subscribe to the service, paying for access to the platform and its features, which include real-time data validation, detailed reporting tools, and automated compliance checks. These features assist clients in saving time and minimizing financial risks related to drug discount programs. Kalderos primarily serves large healthcare systems and pharmaceutical companies in the United States.

Chicago, IllinoisHeadquarters

2016Year Founded

$48.6MTotal Funding

LATE_VCCompany Stage

Consulting, AI & Machine Learning, HealthcareIndustries

51-200Employees

Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Company Match

401(k) Retirement Plan

Paid Vacation

Phone/Internet Stipend

Commuter Benefits

Home Office Stipend

Risks

Strategic shifts by new CSO Daryl Todd may disrupt existing operations.

Involvement in Arkansas litigation could lead to legal challenges or reputational damage.

Accountant shortage in the U.S. may impact Kalderos' financial reporting capabilities.

Differentiation

Kalderos offers a unique SaaS-based drug discount management solution.

The platform uses machine learning to detect inconsistencies in drug discount claims.

Kalderos provides real-time data validation and automated compliance checks.

Upsides

Growing demand for AI-driven compliance solutions in healthcare boosts Kalderos' market potential.

Expansion of 340B program compliance needs increases demand for Kalderos' services.

Collaboration opportunities with hospitals enhance Kalderos' drug discount management capabilities.

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