Machine learning engineer

Description

Job Summary: A Data Professional to lead Data Analytics projects, develop ML models, and apply Generative AI, with a focus on business outcomes and financial impact. Key Highlights: 1. Lead end-to-end Data Analytics projects with a focus on ROI 2. Develop and monitor Statistical and Machine Learning models 3. Explore and apply Generative Artificial Intelligence Description: What do you need to have? * Experience in Data Analytics, Business Intelligence, or related fields. * Advanced SQL knowledge for handling large data volumes, data modeling, and optimizing complex queries. * Proficiency in programming languages for statistical analysis and data modeling, such as Python (with libraries Pandas, Scikit\-learn). * Knowledge of applying advanced statistical methods and the Machine Learning (ML) model lifecycle. * Experience with data visualization tools (Power BI, Tableau). * Business analysis knowledge, with the ability to translate business problems into clear technical and analytical requirements. * Practical experience building and maintaining simple data pipelines (ETL/ELT), and familiarity with Cloud environments (AWS) and/or platforms such as Databricks. * Intermediate English. What do we expect from our team members? * Believe in the joys and benefits of sports; * Ability to lead data projects from start to finish; * Passion for staying updated on the latest trends and technologies in Data & Analytics; * Action- and challenge-oriented; * Passionate about customer satisfaction; * Value teamwork and collective effort; * Proactive and entrepreneurial mindset; * Capable of working responsibly and autonomously; * Courageous in decision-making; * Creative and adaptable in adverse situations. What are the main responsibilities? * Lead end-to-end Data Analytics projects—from scope definition to strategic results presentation for various leadership levels and business areas—prioritizing initiatives with clear Financial Return (ROI) potential. * Act as a Diagnostic Expert, performing complex and rapid Ad\-Hoc analyses to uncover high-value opportunities and critical business issues, ensuring Quick\-Win knowledge delivery. * Develop, implement, and monitor Statistical and Machine Learning (ML) models that become recurring Data Products, delivering continuous value to Business Areas (e.g., propensity, segmentation, recommendation, and prediction models). * Explore and apply Generative Artificial Intelligence (Gen AI) possibilities to accelerate value creation and analytical efficiency. * Rigorously measure and track the financial impact (revenue, cost, or margin effect) of all implemented suggestions and models across Business Areas, ensuring feedback loops and ROI validation. * Propose and implement improvements to data governance and modeling practices, ensuring data is reliable, accessible, and used ethically. 2512160202201868247