Data Scientist

Role Overview: You will be responsible for developing and implementing predictive models and machine learning algorithms to address logistics-related challenges like demand forecasting, route optimization, inventory prediction, and shipment delay analysis. This involves analyzing large volumes of structured and unstructured logistics data from systems like TMS (Transportation Management Systems), WMS (Warehouse Management Systems), and ERP platforms. Key Responsibilities: - Designing and maintaining dashboards and visualizations to monitor supply chain KPIs, operational performance, and model outputs using tools such as Power BI, Tableau, or similar platforms. - Collaborating with logistics, operations, procurement, and IT teams to comprehend business challenges and translate them into data science solutions. - Utilizing SQL, Python, R, and big data tools to extract, clean, and prepare datasets for analysis and modeling purposes. - Deploying models into production and monitoring their performance to ensure accuracy and reliability over time. - Contributing to strategic initiatives like logistics network design, warehouse automation, and sustainability optimization. - Conducting A/B testing and causal inference to evaluate changes in supply chain processes and logistics strategies. Qualifications Required: - Proficiency in SQL, Python, R, and big data tools. - Experience in developing predictive models and machine learning algorithms for logistics-related challenges. - Strong analytical and problem-solving skills. - Excellent communication and collaboration abilities. - Familiarity with tools such as Power BI, Tableau, or similar platforms would be advantageous.,