Senior Data Scientist – AI/ML & MLOps

Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development. Arηs Group was acquired by Accenture in July 2024.

For one of our clients, a major European Union institution, we are looking for an experienced Data Scientist to contribute to the design, development and deployment of advanced Data Science, Machine Learning and AI solutions, someone that:

• Designs and develops the data analytic, data visualisation and artificial intelligence solutions
• Elicits and analyses business needs, requirements, and user stories in context of data science processes
• Supports business experts and product owners during definition of requirements and models development
• Produces data scientist related documentation for models and analytical modules
• Define, collects, and interprets model’s metrics
• Manages data science processes artefacts, models repositories and pipelines
• Contributes to requirements analysis, testing, quality assurance, change management, models deployment
• Tests models and solutions for performance, accuracy, or other business related KPI's
• Design, plan and manage prototypes
• Managing Data Scientists team

• At least 8 years of total IT experience
• At least 5 years of experience in a specific position
• At least 2 certifications among:
  • Certified Analytics Professional (CAP)
  • CDP Data Engineer
  • CDP Data Analyst
  • Microsoft Certified Azure Data Scientist Associate

Knowledge and skills

• Expert in Advanced Analytics, AI, Machine Learning, and data platforms architecting
• Expert in data analytics and visualisation
• Good understanding modern databases engines such as NoSQL, SQL, Document databases and similar.
• Sound knowledge about Python, R or SQL.
• Practical knowledge of Data science, ML models developing and MLOps culture
• Practical experience in building and train Machine Learning and Deep Learning models

Specific requirements

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.