Data Scientist

Tata Consultancy Services; Brussel Hoofdstad; Sur place; EUR 60 000 - 100 000; Plein temps

Résumé du poste

Tata Consultancy Services in Belgium seeks a data science professional to translate business problems into data-driven ML solutions and to deploy models with robust monitoring. You will collaborate with UX and product teams to ensure effective presentation of outputs and insights.

The role emphasizes data preparation, feature engineering, model evaluation, documentation, and compliant analytics aligned with EU and GDPR standards.

Qualifications

• Proven ability to frame business problems, form hypotheses, and design data-driven solutions.
• Excellent communication to present models and insights to varied audiences.
• Commitment to reproducibility and integrity in model development.

Responsabilités

• Collaborate with stakeholders to collect requirements and frame problems as data science hypotheses.
• Develop and deploy advanced data mining and ML solutions with model monitoring.
• Create visualisations to communicate model behaviour and insights.

Connaissances

Python

R

SQL

Spark

Formation

Master's degree in Data Science

Outils

Tableau

SAS

SAP Analytics

Description du poste

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 visualisations 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:

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.