Data Scientist

Opportunity

Responsibilities

• Lead and participate in internal and client workshops to translate operational needs into custom algorithmic AI solutions.
• Analyze complex datasets; design, benchmark, train, and validate ML/DL models for specific use cases under data-sensitivity constraints.
• Build and scale end-to-end ML pipelines for data collection, automated feature engineering, CI/CD deployment, and real-time model monitoring.
• Communicate technical metrics, model behavior, and business ROI to technical and executive audiences.
• Promote AI engineering practices, follow technological developments, and share knowledge.
• Document technical work and adhere to cybersecurity protocols, ethical AI guidelines, and data sovereignty requirements.

Experience and qualifications

• Requires 5–7+ years of professional experience in applied machine learning and AI engineering.
• Requires production-grade ML deployment and maintenance experience, from data ingestion through live inference.
• Requires experience building resilient pipelines and data architectures for real-time operational constraints.
• Requires client-facing discovery or technical ideation workshop experience.
• Requires familiarity with data privacy, security, and sovereignty requirements in defense or critical infrastructure environments.
• Requires a master’s degree in Computer Science, Data Science, Artificial Intelligence, or Engineering.
• Requires high fluency in spoken and written English; French and/or Arabic fluency is a bonus.

Technical qualifications

• Requires an advanced foundation in statistical modeling, machine learning, and deep-learning architectures.
• Requires hands-on experience with Retrieval-Augmented Generation and Model Context Protocol workflows.
• Requires deep Python proficiency and experience with PyTorch or TensorFlow.
• Requires strong SQL and relational-database skills, with working exposure to NoSQL systems.
• Requires production experience with MLflow, Docker, GitLab, Git, Jenkins, Artifactory, SonarQube, and CI/CD automation.
• Requires Agile/Scrum experience using Jira; Java, Apache Spark, or Scala familiarity is a bonus.