IBM Cloud Economics are looking for a Data Scientist to join our team and help us leverage data to optimize the business outcomes at IBM Cloud. You will be responsible for designing, developing, and maintaining data pipelines, databases, and analytics solutions that support the planning, forecasting, and reporting of our third-party expenses, supply chain performance and identifying opportunities to improve the profitability of our Cloud services. You will also be responsible for monitoring and managing global inventories, as well as excess and obsolescence, and ensuring timely and cost-effective delivery of materials and products. You will also be responsible for teaching non-technical team members capabilities to self service automation and reporting.
To be successful as a Data Scientist, you should have a bachelor's degree, and more importantly, experience with data engineering tools, frameworks, and languages, such as SQL, Python, etc. You should also have experience with ERP systems (operational and financial), reporting tools, automation, and ETL processes. Experience with IBM Cognos and/or Apptio would be an advantage. You should also have strong analytical, financial accounting/business acumen, communication, and problem-solving skills, as well as the ability to work in a fast-paced and dynamic environment.
Responsibilities:Provide feedback on design, develop, and maintenance of data pipelines, databases, and analytics solutions that support supply chain operations.
Collect, transform, and integrate data from various sources, such as ERP systems, external APIs, and web scraping
Ensure data quality, reliability, and security throughout the data lifecycle
Build and deploy data models and tools to simulate and optimize supply chain scenarios, as well as help deploy financial models
Create and present automated reports and dashboards on supply chain performance, metrics, and KPIs
Identify and implement opportunities for improving data efficiency, scalability, and usability
Monitor and manage inventory levels and availability across our supply chain network, as well as, monitor and mitigate excess and obsolescence
Provide feedback on the ability to forecast sales and production levels based on historical data, market trends, and customer demand
Identify and implement opportunities for improving efficiency, reducing costs, and increasing customer satisfaction
