Data Scientist

Role description

Freelancer Opportunity - Fulltime/Contract - 2025-01-01 to 2025-06-30

Based in Belgium

Hybrid 2 days/week at the office

As a Data Scientist, you will play a key role in developing end-to-end solutions for AI use cases. By taking up a use case right from the start you will help business define and scope the problem, pick the right AI solution and technology as well as ensure implementation, integration and deployment of your solution to production. You will do so while ensuring that our internal governance processes and best practices are respected. You will also collaborate with other team members in knowledge sharing as well as continuously improving our way of working.

Responsibilities

Develop AI applications using state-of-the-art Gen AI technology.

Translate business requests into AI applications.

Deploy prototypes to production, with support of IT, to obtain reliable, scalable systems.

Identify high-value use cases through data exploration and visualization.

Pilot prototypes in production processes to demonstrate their value.

Present your results in a clear manner and discuss them with multi-functional project teams.

Work in close collaboration with business experts (e.g. for requirement gathering, data source identification, data and process understanding, feature engineering, result validation, etc.), with IT (e.g. for ETL, deployment to production, etc.) and with other Data Scientists in the team (e.g. for knowledge sharing).

Degree & Experience

PhD or master's degree in a quantitative field (Artificial Intelligence, Computer Science, Engineering, Statistics, Mathematics, etc.)

3-5 years of relevant work experience in a business environment

Technical skills

Hands-on experience with Python and Gen AI ecosystem is mandatory

Experience in cloud technology Azure is mandatory

Experience using LLM’s and commonly used libraries to interact with LLM’s (Lang chain, Llama index, ...)

Strong knowledge of state-of-the-art AI and statistical methods.

Proven proficiency in the end-to-end AI project life cycle, including:

Translating business requests into data requirements.

Identifying high-value use cases through data exploration and visualization.

Developing AI solutions (incl. feature engineering, model fitting, etc.).

Deploying scalable AI applications to production.

Any experience with following elements is a big plus:

MLOps practices, e.g. orchestration with Airflow, Gitlab CI/CD, etc.

Containerized deployment of ML products – e.g. docker, pod man, Kubernetes

Languages

Fluent in English and Dutch and/or French is a plus