DATA SCIENTIST

Ayesa Digital; Brussel Hoofdstad; Hybride; EUR 55 000 - 90 000; Plein temps

Avantages offerts par ce poste

EU projects

International team

Expert support

Résumé du poste

Ayesa Digital seeks a Data Scientist to join its international team and contribute to business-driven data science initiatives across European projects. You will collect requirements, frame problems as data science hypotheses, define success metrics, and develop or deploy advanced data mining and ML solutions.

You will collaborate with UX/product teams, build robust data pipelines, develop predictive models for structured and unstructured data, and communicate insights through interpretable

Qualifications

• Knowledge of Python, R, SAS, Spark to design and implement data-driven solutions.
• Knowledge of ML and NLP (Scikit-learn, TensorFlow, PyTorch, Hugging Face) to build predictive and text-based models.
• Knowledge of programming languages (Python, R, SQL) for modelling and automation.
• Knowledge of MLOps practices (CI/CD pipelines, model registry, unit testing) to ensure reproducibility and quality in model delivery.
• Knowledge of BI tools (Tableau, SAS, SAP Analytics) to visualize outputs and insights.
• Knowledge of ETL tools (Talend, Informatica, dbt, Azure Data Factory) to prepare training datasets.
• Knowledge of data storage and query tech (SQL, NoSQL, MongoDB, Hadoop).
• Knowledge of scalable data storage solutions (data lakes, lakehouses).
• Knowledge of analytics applications such as forecasting, recommendations, anomaly detection, or sentiment analysis.
• Knowledge of AI governance principles aligned with EU AI Act requirements.
• Knowledge of AI compliance, bias monitoring, drift detection, model cards.
• Knowledge of GDPR, EU AI Act and designing compliant models.
• Knowledge of experiment design (A/B testing, cross-validation, significance testing).
• Knowledge of model deployment (containerization, APIs, serving frameworks).

Responsabilités

• Collaborate with UX and product teams to specify design requirements for the effective presentation and interpretation of model outputs and insights.
• Develop processes to monitor and analyse data accuracy.
• Identify, collect, and prepare data for analysis, collaborating with Data Analysts to ensure robust data pipelines.
• Produce data models according to specific problem statements.
• Develop and implement machine learning algorithms, statistical models, and scripts to solve business problems.
• Collaborate with Data Analysts and Architects on analytics architecture to support scalability and performance.
• Document tasks and liaise with other teams to address interdependencies.
• Develop visualizations to communicate model behaviour, insights, and performance metrics.
• Design, develop, and evaluate predictive models for structured and unstructured data.
• Elaborate processes to ensure compliant implementation of EU regulations including model cards.
• Maintain deployment and monitoring practices with governance considerations.
• Communicate insights to both technical and non-technical audiences.
• Conduct experiment design (A/B testing, cross-validation, significance testing).
• Deploy and maintain models into production using MLOps, CI/CD, registries, and ensuring reproducibility.

Connaissances

Python

R

SQL

NLP

ML

Formation

Specialty or Azure AI Engineer Associate

Outils

SAS

Spark

Scikit-learn

TensorFlow

PyTorch

Hugging Face

Tableau

SAP Analytics

Talend

Informatica

dbt

Azure Data Factory

MongoDB

Hadoop

Description du poste

Every professional in our company is essential. Thanks to their talent, we continue to expand: today, we are a global team of more than 11,000 people working toward a shared mission.

Ayesa Digital is currently participating in high-impact European Union projects designed to address major European challenges and drive science and innovation. These strategic technological initiatives stand out for their international scope and strong commitment to socially oriented results.

We are looking for a Data Scientist to join our international team and contribute to collaborate with stakeholders to collect requirements, fram business problemas as data science hypotheses, define success metrics, and develop or deploy advanced data mining and machine learning solutions.

Key Responsibilities

• Collaborate with User Experience (UX) and product teams to specify design requirements for the effective presentation and interpretation of model outputs and insights.
• Develop process to monitor and analyse data accuracy.
• Identify, collect, and prepare data for analysis, collaborating with Data Analysts to ensure robust production-grade data pipelines, with a focus on feature engineering and data readiness for modelling.
• Produce data models according to specific problems statements.
• Develop and implement machine learning algorithms, statistical models, and scripts to solve specific business problems.
• Collaborate with Data Analysts and Architects on the design of the analytics architecture to ensure it supports the scalability and performance requirements of data science models.
• Write the different documentation associated with the tasks and liaise with other teams as necessary to address cross-system interdependencies.
• Developing visualizations to communicate model behaviour, key insights, and performance metrics, and collaborating with Data Analysts on integrated dashboard reporting.
• Design, develop, and evaluate predictive models capable of handling both structured and unstructured data, including model selection, training, and hyperparameter tuning
• Elaborate processes to ensure the compliant implementation of regulatory frameworks (e.g., EU AI Act), including model risk management, monitoring for drift/bias, and comprehensive documentation (e.g., model cards).
• Ensure that model development, deployment, and monitoring practices adhere to all relevant regulatory frameworks and internal governance policies.
• Communicate model insights, limitations, and behaviour effectively to both technical and non-technical audiences, and create interpretable outputs and documentation.
• Conduct rigorous experiment design (e.g., A/B testing), employ cross-validation techniques, and perform statistical significance testing to validate findings and model performance.
• Deploy and maintain models into production using Machine Learning Operations (MLOps) practices, including Continuous Integration/Continuous Deployment (CI/CD) pipelines, model registries, and ensuring reproducibility.

What We Are Looking For (Requirements):

• Knowledge of advanced analytics techniques and tools (e.g., Python, R, SAS, Spark) to design and implement data-driven solutions.
• Knowledge of machine learning and natural language processing (e.g., Scikit-learn, TensorFlow, PyTorch, Hugging Face) to build predictive and text-based models.
• Knowledge of programming languages (e.g., Python, R, SQL; Perl optional) commonly used in data science for modelling and automation.
• Knowledge of MLOps practices (e.g., CI/CD pipelines, model registry, unit testing frameworks) to ensure reproducibility and quality in model delivery.
• Knowledge of business intelligence tools (e.g., Tableau, SAS, SAP Analytics) to visualise and communicate model outputs and insights.
• Knowledge of data engineering and ETL processes using tools (e.g., Talend, Informatica, dbt, Azure Data Factory) to prepare training datasets.
• Knowledge of data storage and query technologies (e.g., SQL, NoSQL, MongoDB, Hadoop) to extract and process data at scale.
• Knowledge of designing scalable data storage solutions (e.g., data lakes, lakehouses, distributed stores) for advanced analytics and AI workloads.
• Knowledge of advanced analytics applications such as forecasting, recommendation systems, anomaly detection, or sentiment analysis for business impact.
• Knowledge of AI governance principles (e.g., transparency, explainability, fairness, accountability) aligned with emerging EU AI Act requirements.
• Knowledge of AI compliance, risks, and mitigation practices (e.g., bias monitoring, drift detection, model cards).
• Knowledge of data and AI regulatory frameworks (e.g., GDPR, EU AI Act) and ability to design models compliant with legal and ethical standards.
• Knowledge of experiment design (e.g., A/B testing, cross-validation, significance testing) to validate models rigorously.
• Knowledge of model deployment practices (e.g., containerization, APIs, serving frameworks) to operationalise machine learning solutions.

One of the following or an equivalent certification:

• Specialty or Azure AI Engineer Associate.

What We Offer:

• Prestigious projects within European institutions.
• International, innovative, and multicultural environments.
• Continuous support from a team of experts in EU projects.

If you are ambitious, enthusiastic, and seeking a new professional challenge in international projects with real-world impact, this is the place for you!

In accordance with Organic Law 3/2007 of March 22, the company is committed to promoting the defense and effective application of the principle of equality between men and women, preventing any type of labor discrimination based based on sex, and guaranteeing equal entry opportunities. Furthermore, we promote diversity and reject any discrimination based on race, gender, functional diversity, religion, sexual orientation, gender identity, or any other personal or social condition, striving to build an inclusive and enriching environment.