Senior Data Scientist / Technical Lead

4w4 weeks ago

ITproposal

Brussels, BE · Full-time · €80,000 – €110,000

About this role

This role combines hands-on advanced analytics and machine learning expertise with strategic technical leadership. You will provide coaching and direction within a Data Mining team, transforming ad-hoc analytical requests into scalable data products and reusable solutions.

Day-to-day, you lead complex data science projects from design to production, driving architectural decisions and defining best practices for reproducibility, documentation, and code quality. You also mentor data scientists through code reviews, technical coaching, and collaborative problem-solving.

You work closely with business stakeholders, data engineers, platform teams, and management to structure and prioritize analytical requests. The environment is hybrid, based in Brussels, with multidisciplinary teams focused on analytics, machine learning, and data platform engineering.

This is a senior opportunity to shape technical strategy, improve team capabilities, and develop production-grade ML solutions. You will contribute to knowledge-sharing initiatives and help evolve team processes and standards.

Requirements

Master's degree in Computer Science, Data Science, Engineering, or a related field.
Strong hands-on experience as a Data Scientist or ML Engineer.
Advanced Python expertise, including Pandas, Scikit-learn, and machine learning frameworks.
Experience developing, deploying, and maintaining production-grade ML models.
Strong software engineering background with Git, CI/CD, Docker, APIs, and reusable service architectures (e.g., FastAPI).
Solid SQL skills and experience with data modeling and data analysis.
Experience with Agile/Scrum methodologies and mentoring technical teams.
Excellent analytical, communication, and stakeholder management skills.

Responsibilities

Lead complex data science and machine learning projects from design to production.
Drive architectural and technical decisions for advanced analytics initiatives.
Define and promote best practices for reproducibility, documentation, code quality, and data science methodologies.
Mentor data scientists and analysts through code reviews, technical coaching, and collaborative problem-solving.
Transform one-off analyses into reusable datasets, models, templates, and data products.
Apply data governance and FAIR principles to improve data quality, accessibility, and reusability.
Develop and maintain machine learning solutions in production environments.
Support planning, risk management, prioritization, and delivery tracking with stakeholders.

Benefits

Hybrid working model.
Collaboration with multidisciplinary data, analytics, and platform teams.
Opportunity to mentor and shape team capabilities in advanced analytics and machine learning.