Requirements:

• 3–5 years of professional experience in Python software development, preferably within supply chain, manufacturing, or other data-intensive environments.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field.
• Fluent English required; additional languages are a plus.
• Solid proficiency in SQL for database management, querying, and optimization.
• Familiarity with Microsoft Azure; hands-on experience and certifications are considered an advantage.
• Exposure to machine learning and predictive analytics, particularly in demand forecasting, inventory optimization, and supply chain planning, is a strong asset.
• Excellent analytical and problem-solving skills, supported by a curiosity-driven and proactive mindset.
• High attention to detail and the ability to translate complex data into clear, actionable insights.
• Strong communication abilities, with the capacity to collaborate effectively with both technical and business stakeholders.
• Proven ability to work independently and within cross-functional teams.
• Adaptability to dynamic and fast-changing business environments.
• Travel: 10-20% (as needed, depending on project requirements).

Responsibilities: 

• Design, develop, and maintain Python-based software tools to support evolving business requirements and operational processes.
• Collaborate with business stakeholders to create and implement solutions that enhance demand forecasting, inventory management, and production planning.
• Maintain and optimize SQL databases and Azure-based systems to ensure efficient data integration and support cross-functional analytical needs.
• Partner with data analysts to identify and apply predictive modeling techniques that strengthen supply chain decision-making.
• Conduct regular reviews, testing, and optimization of software tools to ensure scalability, reliability, and alignment with business objectives.
• Integrate new data sources and improve the robustness and accuracy of supply chain analytics through advanced statistical and machine learning methods.
• Analyze data related to cost, productivity, demand, transportation, and inventory to identify opportunities for process optimization and efficiency gains.
• Develop automated scripts and tools to streamline forecasting and inventory tracking processes, incorporating machine learning recommendations where applicable.
• Create customized data visualizations and dashboards tailored to stakeholder requirements to support data-driven decision-making.
• Prepare and maintain comprehensive technical documentation and user guides, ensuring all materials are regularly updated to reflect system enhancements and support effective onboarding and training.