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-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.