Start Date: ASAP
Contract Duration: Renewable
Work Schedule: Up to 20 hours per week
Work Mode: Hybrid

What we need your help with

The selected candidate will:

Develop a solid framework for training and validating generative AI models.
Apply prompt engineering techniques to optimize GenAI model outputs in terms of segmentation accuracy, interpretability, and alignment with business objectives.
Participate in data verification and validation to ensure input quality and model reliability.
Support the preparation, transformation, and enrichment of user engagement data, contributing to the development of a scalable data pipeline with meticulous attention to each stage.
Conduct technical audits and validate models to strengthen their robustness and statistical accuracy, demonstrating strong analytical rigor in identifying and resolving issues.
Analyze and audit proof-of-concept models and propose continuous improvements based on performance and interpretability.
Contribute to transforming behavioral signals and content interaction data to better reflect real user behavior (e.g., recency effect, content saturation, engagement decay).
Identify and apply appropriate modeling techniques to achieve segmentation and personalization goals.
Guide data engineering and variable selection while identifying and addressing data-related constraints.
Help communicate model results and rationales to non-technical stakeholders by producing natural language explanations of GenAI model decisions in a clear, business-relevant format.

What we are looking for

Minimum of 5 years of experience in data science within an industrial environment, ideally in digital media, advertising, or marketing analytics.
Master’s degree or higher in a STEM field (Statistics, Mathematics, Computer Science, Economics, or related discipline).
Hands-on experience with generative AI (GenAI) models, both in development and production settings.
Conceptual knowledge of Customer Data Platforms (CDP) and/or Data Management Platforms (DMP), with practical experience handling these types of data.
Strong skills in SQL and Python, with proven experience applying agile practices and collaborative coding standards to build and maintain scalable, well-structured team codebases.
Proficiency in advanced analytics techniques and machine learning algorithms.
Strong attention to detail and a methodical, rigorous approach to data analysis and model development.
Ability to proactively identify problems and propose innovative solutions to complex challenges.
A reputation for integrity, reliability, and commitment within collaborative team projects.
Knowledge of French is considered an asset.