Data Scientist Magenta Ev Solutions Pvt. Ltd
Magenta Ev Solutions Pvt. Ltd
Office Location
Full Time
Experience: 3 - 3 years required
Pay:
Salary Information not included
Type: Full Time
Location: Maharashtra
Skills: Predictive modeling, Machine Learning, Logistics, Demand forecasting, Route Optimization, Data Analysis, Data Visualization, supply chain management, SQL, Python, R, big data, Power Bi, Tableau, Causal inference, Inventory Prediction, Shipment Delay Analysis, AB testing
About Magenta Ev Solutions Pvt. Ltd
Job Description
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. Your role will also include 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. Collaboration with logistics, operations, procurement, and IT teams will be essential to comprehend business challenges and translate them into data science solutions. You will utilize SQL, Python, R, and big data tools to extract, clean, and prepare datasets for analysis and modeling purposes. Additionally, deploying models into production and monitoring their performance to ensure accuracy and reliability over time will be part of your responsibilities. In this position, you will contribute 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 will also be a key aspect of your role. This is a full-time position with benefits including health insurance and provident fund. The work schedule is during day shifts and the work location is in person.,