Road & Capacity Scientist (m/f/d)

h3bDHL Express
- a company that connects people /b /h3 pWhat makes DHL great? Our People We know each employee’s individual contributions make us the #1 Express Delivery and Logistics Company in the world. /p pDistinguished as No.1 World’s Best Workplace™ by Great Place to Work and Fortune Magazine DHL Express is committed to maintaining an environment that empowers every team member to make the greatest possible impact on our business. Our corporate culture is about personal commitment – to our business, to each other and to our global communities. DHL is dedicated to being a great place to work. /p pDHL Express is looking for a bRoad Capacity Scientist /b in Diegem. /p br/ pRoad Capacity Scientist is responsible for designing, analyzing, and optimizing DHL’s road network using advanced modeling, optimization tools, and data science techniques. This role translates operational data into actionable network decisions that improve cost, service, asset utilization, and customer experience across linehaul, hubs, and first/last mile operations. /p br/ pAs DHL’s processes are increasingly underpinned by data, this role is a key contributor to business process optimization and network transformation. They will extract meaningful insights, identify predictive opportunities, and develop automation possibilities that support better decision‑making across the European ground network. The role will identify, develop, and implement analytical models, prototypes, and best‑practice methods for business users, ensuring that modeling outputs are practical, implementable, and aligned with business objectives. /p br/ pThey will serve as the bridge between network design, operations, technology, and data science, helping optimize processes, systems, and methods using relevant operational data. /p br/ h3bHow will you contribute to the success of DHL? /b /h3 ul liExecution of data analytics projects with very limited or no guidance from a supervisor/project lead. /li liAnalysis of the business processes as well as the derivation of problem areas and analytical requirements. /li liSourcing initial cleansing and preparation of large amounts of data. Identification and collection of required data, as well as data cleansing. /li liDevelop statistical/mathematical models (e.g., time series analyses, operations research methods, machine learning). /li liDevelop implementation data pipelines. Modify data analysts' code to prepare for deployment. /li liClarify data needs and take action with the IT organization. /li liSupport the identification of more efficient ways to store, source, query, and activate data. /li liDeploy best practices to optimize processes, systems, and methods using relevant data. /li liSupport the definition of project contents, deliverables, and timelines of data analytics projects in terms of data needs. /li liSource, prepare, pre-analyze, and cleanse data as per business needs. /li liLargely independent preparation and presentation of results and implications of Data Analytics projects. /li liManage contact with internal and external customers on data analytics topics at the work and specialist level and with middle management in the context of current and future analytics projects. /li liSupport in onboarding and coaching junior data science and engineering colleagues as part of the project. /li liActively contribute to team responsibilities and support business development activities on request. /li liProduce accurate outcomes of analytics tasks and communicate as such (accurate does not imply success). /li liManage contact with internal and external customers on data analytics topics at the work and specialist level. /li liManage contact with middle management in the context of current and future analytics projects. /li liRelationship management with business departments about relevant analytics topics, as well as internal and external expert networks in the data analytics field. /li /ul pbDo you have what it takes? /b /p ul liUniversity degree in Mathematics, Statistics, Physics, Computer Science or related natural sciences / technical degree, or comparable qualification. /li liMore than 4 years of experience working in data science, mathematics, statistics, research, and/or IT. /li liKnowledge of machine learning, statistics, data visualization, and cleansing techniques, as well as data pipelining and big data technologies. /li liExperienced in analyzing complex datasets and building mathematical models, also in a practical context. /li liHaving programming skills and/or experience in scripting languages (Python, R, or Java) and good experience in working with databases (relational, big data) and data pipelines. /li liAbility to produce creative and analytical solutions based on clear and thorough analyses of complex processes and business relationships. /li liComfortable explaining complex analytical and technical content to various audiences. /li liEnergetic and success-oriented problem solver. /li l