Data Scientist

Tata Consultancy Services; Brussel Hoofdstad; Sur place; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Tata Consultancy Services Belgium is seeking a data science professional to collaborate with stakeholders, frame business problems as data-driven hypotheses, and develop machine learning solutions. You will design scalable data pipelines, build models for structured and unstructured data, and ensure governance and regulatory alignment (EU AI Act).

You will lead experiments, deploy ML models in production, and communicate insights with clear documentation and dashboards, enabling informed

Qualifications

• Ability to frame business problems, form hypotheses, and design solutions.

Responsabilités

• Collaborate with stakeholders to collect requirements, frame business problems as data science hypotheses, define success metrics, and develop or deploy advanced data mining and machine learning solutions.
• 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 production-grade data pipelines.
• Produce data models according to specific problem statements.
• Develop and implement machine learning algorithms, statistical models, and scripts to solve specific business problems.
• Collaborate with Data Analysts and Architects on analytics architecture for scalability and performance.
• Write the documentation and liaise with other teams to address cross-system interdependencies.
• Develop visualizations to communicate model behaviour, insights, and performance metrics, and collaborate on integrated dashboard reporting.
• Design, develop, and evaluate predictive models for structured and unstructured data, including model selection, training, and hyperparameter tuning.
• Elaborate processes to ensure compliant implementation of regulatory frameworks (e.g., EU AI Act), including model risk management and drift/bias monitoring.
• Ensure model development, deployment, and monitoring adhere to regulatory frameworks and governance policies.
• Communicate insights and limitations to technical and non-technical audiences, creating interpretable outputs and documentation.
• Conduct rigorous experiment design (A/B testing, cross-validation), and significance testing to validate findings and model performance.
• Deploy and maintain models in production using MLOps practices, CI/CD pipelines, and model registries.

Connaissances

Problem framing

Storytelling

Experiment design

Multilingual meetings

English fluency

Team collaboration

Autonomy

Results-oriented

Communication

Analytical mindset

Adaptability

Presentation

Decision making

Outils

Python

R

SQL

SAS

Spark

Scikit-learn

TensorFlow

PyTorch

Hugging Face

Tableau

dbt

Azure Data Factory

Informatica

Talend

MongoDB

Description du poste

Location: Brussels, Belgium

Company: Tata Consultancy Services (TCS) Belgium

Employment Type: Full-time

Nature of the Tasks

• Collaborate with stakeholders to collect requirements, frame business problems as data science hypotheses, define success metrics, and develop or deploy advanced data mining and machine learning solutions.
• 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 problem 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.
• Develop visualizations to communicate model behaviour, key insights, and performance metrics, and collaborate 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.

Specific Expertise and Technologies

• 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.

Minimum Level of Expertise

• Normal

Certification and/or Standards

Optional: A certification directly related to the requested profile.

Optional for 'Normal' level and above related to Public Cloud: One of the following or an equivalent certification:

• AWS Certified Machine Learning – Specialty
• Azure AI Engineer Associate

Skills

• Ability to frame business problems, form hypotheses, and design solutions.
• Skill in presenting models and insights clearly to all audiences.
• Commitment to accuracy, reproducibility, and integrity in models.
• Ability to design and test approaches for complex challenges.
• Competence in managing experiments, pipelines, and deliverables.
• Drive to explore new algorithms, frameworks, and AI methods.
• Ability to interpret analytical findings to tell a compelling story that clearly prescribes business actions and drives strategic decision-making.
• Flexibility to adopt new ML/AI tools and frameworks.
• Capability to frame model results into compelling business stories.
• Ability to participate in multilingual meetings.
• Ability to understand, speak and write English, optionally French as an additional asset.
• Excellent interpersonal skills.
• Ability to work in a team as well as autonomously.
• Results-oriented mindset, focused on delivering.