Machine Learning Engineer

Description

Job Summary: A data professional to lead Data Analytics projects, develop ML models, and apply Generative AI—focused on business outcomes. Key Highlights: 1. Lead end-to-end Data Analytics projects with a focus on ROI. 2. Develop and monitor Machine Learning models as data products. 3. Explore and apply Generative Artificial Intelligence. Description: What You Need: * Experience in Data Analytics, Business Intelligence, or related fields. * Advanced SQL knowledge for handling large-scale data, data modeling, and optimizing complex queries. * Proficiency in programming languages for statistical analysis and data modeling, such as Python (with libraries including Pandas, Scikit\-learn). * Knowledge of 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 proficiency. What We Expect From Our Team Members: * Believe in the joys and benefits of sports; * Ability to lead data projects from inception to completion; * 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 on Investment (ROI). * Serve as a Diagnostic Expert, conducting complex and rapid Ad\-Hoc analyses to uncover high-value opportunities and critical business issues—ensuring rapid knowledge delivery (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) capabilities to accelerate value creation and analytical efficiency. * Rigorously measure and track the financial impact (revenue, cost, or margin effect) of all implemented recommendations 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. 2512160202201868247