Cnexia Tech is driven by a culture strongly focused on performance and competitiveness, where ambition, challenge and measurable results are key drivers of success. This dynamic is complemented by a collaborative dimension, promoting trust, teamwork and close managerial support. It also integrates an innovation component that encourages initiative, adaptability and experimentation in a fast-evolving technological environment. Finally, a structured organizational culture underpins the whole by ensuring process rigor, reliability and operational efficiency.

• 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