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

Twtspain; Warszawa; Híbrido; PLN 140 000 - 220 000; Tempo integral

Resumo da oferta

Frontex seeks a Data Scientist at an Advanced level for a 45-month mission based in Warsaw with half the time onsite and half remote. The role requires a Master’s degree, 8+ years relevant experience, and security clearance; English at C1 and strong Azure data tools experience are expected.

The candidate will develop advanced analytics, ML models, and responsible AI practices within a regulated public-sector environment.

Qualificações

• End-to-end Data Science and ML solution design, data prep, feature engineering, model development, validation, deployment, monitoring.

Responsabilidades

• Design and deploy AI/ML solutions in hybrid cloud environments.
• Translate business needs into measurable DS requirements and document results.
• Lead and collaborate with cross-functional teams to deliver scalable analytics.

Conhecimentos

Data science

ML models

Python

MLOps

Communication

Documentation

Problem solving

Leadership

Formação académica

Master’s degree

Ferramentas

Azure Machine Learning

TensorFlow

PyTorch

Docker

Kubernetes

Descrição da oferta de emprego

Data Scientist, Level advanced (50% remote, Warsaw) – Frontex

Profile: Data Scientist - Level Advanced.

Place of performance: 50% onsite/50% remote.On-site service provision at Frontex Headquarters in Warsaw.

Duration of the mission: 45 months.

Security Clearance: Is required (required from day 45 of assignment) - CONFIDENTIEL UE/EU CONFIDENTIAL.

Travel: not foreseen.

Minimum level of education: Level 7 (Master’s Degree required).

Minimum English language skills: C1.

Minimum IT relevant experience: 8 years (5 years in relevant roles).

Award criteria: 65% Quality/35% Price.

Minimum required scoring for interview (%): 70%.

Rate: Flexible. The rate offered depends on the candidate’s level, in accordance with the European public grading system. Further details are available upon discussion.

NWH: 220 days x 3 years + 165days 4th year

EWH: 2 days x 4 years.

Required technical certificates

At least 2 certifications among:

• [1] Certified Analytics Professional (CAP).
• [2] CDP Data Engineer.
• [3] CDP Data Analyst.
• [4] Microsoft Certified Azure Data Scientist Associate.

Or for any listed above, an equivalent alternative certification recognized internationally (subject to acceptance as a valid credential by the Contracting Authority).

Knowledge and Skills

Knowledge:

• [01] Has performed role of Data Scientists in development of at least 2 advanced analytic and AI solutions, design common data model, AI and Machine Learning based solutions deployment.
• [02] Expert in Advanced Analytics, AI, Machine Learning, and data platforms architecting
• [03] Expert in data analytics and visualization.
• [04] Well understanding of sophisticated models for real-time, predictive maintenance, forecasting, recommendation, video and picture analytics.
• [05] Well understanding and hands-on, practical experience in various data visualization techniques and tools.
• [06] Well understanding and hands-on, practical experience in analyse huge amounts of data, both structured and unstructured.
• [07] Good understanding modern databases engines such as NoSQL, SQL, Document databases and similar.
• [08] Sound knowledge about Python, R or SQL.
• [09] Familiar with Data Engineering and Data Quality aspects.
• [10] Well understanding market trends in area of Advanced Analytic and AI.
• [11] Well understanding AI Ethic, risks and advantages.
• [12] Familiar with CRISP DM, TDSP or other Data Scientist methodologies.
• [13] General knowledge of Advanced Analytic data science Processes e.g., TDSP, CRISP, KDD.
• [14] General knowledge of cloud and on-premises Big Data and Advanced Analytic platforms, Data Mining. and ML Frameworks.
• [15] General knowledge about Data Driven Organisations and role of Centre of Excellence in Enterprise-wide organizations.

Skills:

• [16] Presents excellent communication skills.
• [17] Holds ability to document effectively.
• [18] Presents problem solving skills.
• [19] Presents leadership skills.
• [20] Practical knowledge of Data science, ML models developing and MLOps culture.
• [21] Analytical skills and understanding of Data scientist process.
• [22] Practical experience in building and train Machine Learning and Deep Learning models.

Specific requirements

• [01] Practical experience in the design and implementation of end-to-end Data Science and Machine Learning solutions, including problem framing, data preparation, feature engineering, model development, validation, deployment, monitoring and iterative improvement.
• [2] Practical experience with Azure-based Data Science and AI services, such as Azure Machine Learning, Azure AI Foundry, Azure OpenAI or comparable AI/ML platforms, including experimentation, evaluation, deployment and lifecycle management.
• [3] Practical experience with MLOps and DevOps practices for Data Science, including version control, reproducible pipelines, model and artefact registries, CI/CD, automated testing, monitoring, documentation and controlled promotion between environments.
• [4] Advanced practical experience with Python-based Data Science and Machine Learning development, using relevant libraries and frameworks such as pandas, scikit-learn, PyTorch, TensorFlow, MLflow, Spark/PySpark or equivalent technologies.
• [5] Practical experience across multiple Data Science and AI use-case families, such as forecasting and predictive analytics, classification, clustering, anomaly detection, natural language processing, document intelligence, information extraction, retrieval-augmented generation and/or computer vision/video analytics.
• [6] Experience working with heterogeneous data sources, including APIs, relational or non‑relational databases, files, open‑source data, semi‑structured or unstructured documents and large‑scale datasets, with appropriate consideration of data quality, lineage, metadata and reproducibility.
• [7] Ability to design and operate Data Science and AI workloads in secure, controlled or restricted environments, including hybrid cloud, on‑premises and isolated/air‑gapped environments, using containerised deployment where appropriate (e.g. Docker, Kubernetes/OpenShift), including environments using CPU/GPU compute and with limited or no external connectivity.
• [8] Ability to translate business needs and ambiguous problem statements into measurable Data Science requirements, propose realistic technical approaches, develop prototypes, explain results and limitations to non‑technical stakeholders and produce clear technical and model documentation.
• [9] Practical understanding of responsible AI and model risk, including data protection, bias, explainability, traceability, human oversight, model limitations and appropriate governance controls, particularly in regulated or public‑sector environments.
• [10] 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, is considered an advantage.

Typical tasks and responsibilities

• [1] Practical experience in the design and implementation of end-to-end Data Science and Machine Learning solutions, including problem framing, data preparation, feature engineering, model development, validation, deployment, monitoring and iterative improvement.
• [2] Practical experience with Azure-based Data Science and AI services, such as Azure Machine Learning, Azure AI Foundry, Azure OpenAI or comparable AI/ML platforms, including experimentation, evaluation, deployment and lifecycle management.
• [3] Practical experience with MLOps and DevOps practices for Data Science, including version control, reproducible pipelines, model and artefact registries, CI/CD, automated testing, monitoring, documentation and controlled promotion between environments.
• [4] Advanced practical experience with Python-based Data Science and Machine Learning development, using relevant libraries and frameworks such as pandas, scikit‑lean, PyTorch, TensorFlow, MLflow, Spark/PySpark or equivalent technologies.
• [5] Practical experience across multiple Data Science and AI use‑case families, such as forecasting and predictive analytics, classification, clustering, anomaly detection, natural language processing, document intelligence, information extraction, retrieval‑augmented generation and/or computer vision/video analytics.
• [6] Experience working with heterogeneous data sources, including APIs, relational or non‑relational databases, files, open‑source data, semi‑structured or unstructured documents and large‑scale datasets, with appropriate consideration of data quality, lineage, metadata and reproducibility.
• [7] Ability to design and operate Data Science and AI workloads in secure, controlled or restricted environments, including hybrid cloud, on‑premises and isolated/air‑gapped environments, using containerised deployment where appropriate (e.g. Docker, Kubernetes/OpenShift), including environments using CPU/GPU compute and with limited or no external connectivity.
• [8] Ability to translate business needs and ambiguous problem statements into measurable Data Science requirements, propose realistic technical approaches, develop prototypes, explain results and limitations to non‑technical stakeholders and produce clear technical and model documentation.
• [9] Practical understanding of responsible AI and model risk, including data protection, bias, explainability, traceability, human oversight, model limitations and appropriate governance controls, particularly in regulated or public‑sector environments.
• [10] 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, is considered an advantage.