Data scientist - Rabat

• Design and train predictive models, including deep learning models, to optimize key business outcomes and address complex challenges. • Engineer impactful features and build robust, automated data pipelines to support model training and inference. • Rigorously analyze model performance and systematically fine-tune models to maximize business metrics. • Build and deploy scalable, low-latency, production-grade applications to serve models in a production cloud environment. • Use a production-first mindset to consult on project feasibility and help shape technical strategy, balancing performance, time, and cost. • Design and validate new processes, products, and advanced ML techniques through experimentation and testing. • Analyze large structured and unstructured datasets to identify insights and propose strategies to address complex technical challenges. • Communicate complex findings and strategic recommendations to technical and non-technical audiences concisely and effectively. • Proactively engage with team members to accomplish individual and groups goals, providing technical mentorship to other data scientists. • Continuously seek new opportunities to develop and apply new skills towards the improvement of data science processes and technical methodologies. • Establish comprehensive monitoring for model performance and data drift, using insights to drive continuous improvement. • Document complex model architectures, deployment processes, and data pipelines to ensure maintainability. 
University or college degree in engineering, computer sciences, physics, mathematics or relevant experience in an equivalent domain Excellent communication skills in English Minimum of 3+ years’ as a Business Data Scientist or similar role End-to-end Machine Learning Development - experience with training, evaluating, iterating, and deploying ML and DL models into production environments Model Evaluation & Performance Tuning - ability to evaluate models using best practice offline methods and online A/B testing to improve business KPIs Model Serving Application Development - skill to build scalable, low-latency Python applications to serve models and additional optimizations for real-time inference Applied Machine Learning / Deep Learning - in-depth knowledge of ML / DL algorithms and frameworks (scikit-learn, PyTorch, etc.) to make applied modeling decisions and articulate trade-offs Feature Engineering - expertise in designing impactful features and integrating into processes for model serving, using advanced SQL for data wrangling and Pandas for preprocessing