Data Scientist Engineer M/V/X

WERAANBIEDING UIT EEN ANDERE REGIO; Diegem; Sur place; EUR 60 000 - 90 000; Plein temps

Résumé du poste

Devoteam is seeking a Data Scientist/Engineer to design and implement robust data platforms on Azure, AWS or Google Cloud, including Databricks. You will gather and prepare data, research and build ML models, and collaborate with clients and internal teams across engagements.

The role offers hands-on delivery across industries with clear deliverables and timelines. You will spend about half your time working directly with clients, shaping solutions and driving impact through data-driven insights

Qualifications

• Experience designing data platforms using major cloud providers (Azure, AWS, GCP) and Databricks.
• Strong data management and data wrangling skills for analysis-ready datasets.
• Familiarity with machine learning modeling, evaluation, and iteration.

Responsabilités

• Platform Setup: Design and implement robust data platforms using Azure, AWS or Google Cloud and Databricks.
• Data Management: Gather, prepare, and manage data for analysis.
• Model Research: Investigate and select appropriate machine learning models.
• ML Modeling: Develop and refine machine learning models.
• Client Collaboration: Spend approximately 50% of your time working directly with clients, either independently or as part of a team.

Description du poste

Are you passionate about data-driven platforms, machine learning models, and innovative applications? As a Data Scientist/Engineer at Devoteam, you'll play a hands-on role in delivering impactful solutions across various clients and industries. Each mission we undertake has clear deliverables and deadlines, ensuring you gain diverse experience. For example, one month you might develop a predictive maintenance model, and the next, a customer segmentation model for a retail client.

Key Responsibilities

• Platform Setup: Design and implement robust data platforms using Azure, AWS or Google Cloud and Databricks.
• Data Management: Gather, prepare, and manage data for analysis.
• Model Research: Investigate and select appropriate machine learning models.
• ML Modeling: Develop and refine machine learning models.
• Client Collaboration: Spend approximately 50% of your time working directly with clients, either independently or as part of a team.