Machine learning engineer

Description

Job Summary: A Data Professional to lead Data Analytics projects, serve as a diagnostic expert, and develop statistical and machine learning models—delivering continuous business value. Key Highlights: 1. Lead end-to-end Data Analytics projects. 2. Serve as a Diagnostic Specialist conducting ad-hoc analyses. 3. Develop statistical and ML models that become Data Products. Description: What You Need: * 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. * Hands-on experience building and maintaining simple data pipelines (ETL/ELT), and familiarity with Cloud environments (AWS) and/or platforms like Databricks. * Intermediate English. What 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 current with 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). * Serve as a Diagnostic Specialist, conducting complex and rapid ad\-hoc analyses to uncover high-value opportunities and critical business issues—ensuring quick knowledge-based wins (Quick\-Win). * 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 trustworthy, accessible, and ethically used. 2511140202461868247