Supply Chain Data Scientist
Within the Hardware Service unit (Service Area Repair) the Supply Chain Data Scientist is responsible for developing data analytics within our spare part management business environment. The Data Scientist need to establish a foundation of data driven supply chain, where he/she can support the business with insight in our supply chain and solving challenging operational issues driven by analytics. Furthermore, the expectation is that the data scientist pro-actively drive improvements and innovation by creating insights into the data that provides new perspectives.
The data scientist will collaborate with departments within Hardware Services and outside our unit as well, like tool, group analytics and processes.
Responsibilities & Tasks:
· Gather, interpret and analyze data by using statistical techniques and develop value business insights and visualizations
· Establish structural visualization of the Supply Chain in order to fundamentally support the operations with the day to day business
· Use analytical and statistical methods to solve complex problems and drive business decisions and ensure continuous improvements within the business
· Collect, clean, structure the right data from different sources for modelling purposes needed for the global Supply Chain Optimization program
· Based on own insight of the quality of the data, improve data quality and collaborate with the business and data management department to structurally improve data entry
· Assess opportunities and promote use of Big Data and advanced analytics for the Ericsson Hardware Service Area (HWS)
· Support with use-cases the data analytics way forward of Hardware Services
· Formulate and define scope of data analytics based on both business needs and a thorough understanding of business processes
· Advance Ericsson HWS analytics maturity to move beyond descriptive analytics into predictive, prescriptive and fully automated/algorithmic capabilities
· Collaborates and/or participates with analytical drivers in Ericsson units, like tools, group analytics and Sourcing/Supply
· Establish advanced analytics support and infrastructure (expertise, tools, techniques, process standard, etc.) across the HWS organization
Skills and Capabilities:
· Strong analytical skills with the ability to collect, organize and analyze significant amounts of data with attention to detail and accuracy
· Proven working experience as a data analyst or business data analyst in supply chain environment
· Adept at using big data to solve business problems
· Mix of robust business and supply chain understanding (preferable in spare part management), architecture knowledge and advanced analytical techniques understanding
· Understanding of using various reliability and statistical technologies and methods
· Strong knowledge of and experience with Analytics and Business Intelligence Platforms (Tableau required) and databases
· Excellent oral and written skills to effectively communicate ideas, processes, status and provide clear documentation to the management and leadership team
· Ability to be flexible within a fast-moving work environment and juggle multiple priorities at the same time
· Ability to guide teams to solve abstract problems without precedent – diagnose and resolve ambiguous problems
· Ability to enter new territory, push data analytics forwards in terms of demonstrating analytics capabilities and making them work for Ericsson HWS
· Ability to engage internally within HWS, engage with senior stakeholders and engage with different cultures in the global job
· 3-7 years of work experience within data science, preferable within a supply chain environment
· Experience in a predictive and decision support analytic function
· Proven track record of using analytics techniques to contribute to business problems and implementation of data solutions within a supply chain
· Master’s Degree (Computer Science, Statistics, Data Management, Supply Chain Management or related field preferred)
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Primary country and city: Netherlands (NL) || || Rijen || ServEng
Req ID: 259888