Data Scientist, Level advanced (50% remote, Warsaw) – EU Public Institutions

The White Team•Capon Bridge, WV

2h•Hybrid

About The Position

Frontex is seeking an advanced-level Data Scientist to provide services at its headquarters in Warsaw, with a 50% onsite and 50% remote arrangement. The mission duration is 45 months. Security clearance at CONFIDENTIEL UE/EU CONFIDENTIAL level is required from day 45 of the assignment. Travel is not foreseen. The role involves designing and implementing end-to-end data science and machine learning solutions, applying Azure-based AI and data science services, and establishing MLOps and DevOps practices. The work spans diverse data sources and AI use cases, including predictive analytics, natural language processing, document intelligence, and computer vision. The Data Scientist will also address responsible AI and model risk and may contribute to cross-organisational Data & AI enablement. The position requires a Master’s degree (Level 7), C1 English, at least eight years of relevant IT experience (including five years in relevant roles), and at least two specified or internationally recognized equivalent technical certifications. Rates are flexible and depend on the candidate’s level under the European public grading system; further details are available on discussion.

Requirements

Master’s degree (Level 7).
Minimum C1 English language proficiency.
At least 8 years of relevant IT experience, including 5 years in relevant roles.
At least two technical certifications among Certified Analytics Professional (CAP), CDP Data Engineer, CDP Data Analyst, and Microsoft Certified Azure Data Scientist Associate, or internationally recognized equivalent certifications subject to Contracting Authority acceptance.
Experience as a Data Scientist contributing to development of at least two advanced analytics and AI solutions, including common data model design and deployment of AI- and Machine Learning-based solutions.
Expertise in Advanced Analytics, AI, Machine Learning, data platform architecture, data analytics, and visualization.
Understanding of sophisticated models for real-time analytics, predictive maintenance, forecasting, recommendations, and video or picture analytics.
Practical knowledge of data visualization techniques and tools and experience analysing large structured and unstructured datasets.
Understanding of NoSQL, SQL, document databases, or similar database engines.
Knowledge of Python, R, or SQL; familiarity with Data Engineering and Data Quality.
Understanding of Advanced Analytics and AI market trends, AI ethics, risks, and benefits.
Familiarity with CRISP-DM, TDSP, KDD, or other Data Scientist methodologies and processes.
General knowledge of cloud and on-premises Big Data and Advanced Analytics platforms, data mining, and Machine Learning frameworks.
General knowledge of data-driven organisations and the role of a Centre of Excellence in enterprise-wide organisations.
Excellent communication, documentation, problem-solving, leadership, and analytical skills.
Practical knowledge of Data Science, Machine Learning model development, and MLOps culture; experience building and training Machine Learning and Deep Learning models.
Practical experience designing and implementing end-to-end Data Science and Machine Learning solutions.
Practical experience with Azure services such as Azure Machine Learning, Azure AI Foundry, or Azure OpenAI, or comparable AI/ML platforms.
Practical experience with MLOps and DevOps for Data Science, including reproducible pipelines, CI/CD, testing, monitoring, and lifecycle controls.
Advanced practical experience with Python Data Science and Machine Learning tools such as pandas, scikit-learn, PyTorch, TensorFlow, MLflow, Spark, or PySpark, or equivalents.
Experience across multiple Data Science and AI use-case families.
Experience working with APIs, databases, files, open-source data, semi-structured or unstructured documents, and large-scale datasets.
Ability to work with secure or restricted environments, including hybrid cloud, on-premises, or isolated/air-gapped environments and containerised deployment where appropriate.
Ability to convert business needs into measurable requirements, explain findings and limitations to non-technical stakeholders, and document technical and model work.
Practical understanding of responsible AI and model risk, including data protection, bias, explainability, traceability, human oversight, and governance.

Nice To Haves

Experience working in or supporting a Data & AI Centre of Excellence, AI enablement function, analytics platform team, or similar cross-organisational capability, including development of reusable approaches, standards, templates, or technical components.

Responsibilities

Design and implement end-to-end Data Science and Machine Learning solutions, from problem framing and data preparation through feature engineering, development, validation, deployment, monitoring, and iterative improvement.
Use Azure-based Data Science and AI services or comparable platforms for experimentation, evaluation, deployment, and lifecycle management.
Apply MLOps and DevOps practices, including version control, reproducible pipelines, registries, CI/CD, automated testing, monitoring, documentation, and controlled promotion between environments.
Develop Data Science and Machine Learning solutions using Python and relevant libraries and frameworks.
Address a range of Data Science and AI use cases, including forecasting, predictive analytics, classification, clustering, anomaly detection, NLP, document intelligence, information extraction, retrieval-augmented generation, and computer vision or video analytics.
Work with heterogeneous data sources and large-scale datasets while considering data quality, lineage, metadata, and reproducibility.
Design and operate Data Science and AI workloads in secure, controlled, restricted, hybrid, on-premises, or isolated environments, using containerised deployment and CPU or GPU compute where appropriate.
Translate business needs and ambiguous problems into measurable Data Science requirements, propose technical approaches, develop prototypes, explain results and limitations to non-technical stakeholders, and produce technical and model documentation.
Apply responsible AI and model-risk practices concerning data protection, bias, explainability, traceability, human oversight, model limitations, and governance.
Contribute to a Data & AI Centre of Excellence or similar capability by developing reusable approaches, standards, templates, or technical components, where applicable.