CONTROL TOWER DATA SCIENCE SR. ANALYST

Control Tower Data Science Sr. Analyst - Analyst to lead data architecture, analytics, and visualization efforts for our Logistics Control Tower. This role is responsible for designing and implementing integrated data pipelines, developing dashboards and KPIs, and building end-to-end solutions—spanning both backend (data integration, processing) and frontend (visualization, reporting) components. This professional will play a pivotal role in transforming real-time logistics data into actionable insights, supporting operational efficiency, service level performance, and strategic planning initiatives across the supply chain. Responsibilities: Design and implement data infrastructure for the Logistics Control Tower with a comprehensive End-to-End (E2E) Supply Chain perspective, ensuring integration across sourcing, production, warehousing, transportation, and customer delivery. Develop and manage ETL/ELT pipelines to consolidate data from multiple systems (e. G. , WMS, TMS, ERP, telematics, external partners), enabling real-time visibility and decision-making. Create and maintain advanced dashboards, reports, and control panels using Power BI, providing dynamic insights into logistics KPIs such as OTIF, transit time, route performance, inventory turnover, and exception management. Actively collaborate with cross-functional logistics teams (transport, warehousing, planning, customer service) to understand operational challenges, perform root cause analysis, and co-create analytical solutions tailored to business needs. Promote a data-driven culture by translating complex data into intuitive visual narratives and actionable recommendations. Ensure data integrity, security, and documentation, maintain alignment with internal governance and compliance frameworks. Lead continuous improvement initiatives by leveraging advanced analytics, predictive models, or machine learning where appropriate, to anticipate disruptions and optimize processes. Support integration of analytics into broader S&OE and S&OP routines, enabling proactive supply chain orchestration and scenario-based planning. Qualifications: Required/ Minimum Qualifications: Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Logistics, or related fields. 3–5 years of hands-on experience in data analytics, business intelligence, or data engineering, preferably within supply chain or logistics. Advanced proficiency in Python (pandas, NumPy, PySpark, etc. ), SQL, and Power BI. Experience with API integrations, cloud platforms (Azure/AWS/GCP), and data warehousing tools. Strong knowledge of logistics KPIs, supply chain processes, and control tower concepts. Ability to manage backend (data modeling, integration) and frontend (UI/UX dashboards) development lifecycle. Fluent in English; additional languages (e. G. , Portuguese, Spanish) are a plus. Additional / Preferred Qualifications: - Solid understanding of logistics operations (transportation, warehousing, distribution planning) and end-to-end supply chain processes, including inventory management, order fulfillment, and customer service. Strong knowledge of supply chain systems architecture, including integration across TMS, WMS, ERP (e. G. , SAP, Oracle), and external data sources (APIs, EDI). Familiarity with cloud-based platforms (Azure, AWS, GCP) and experience in managing data lake, data warehouse, or big data environments. Exposure to machine learning or predictive analytics techniques, particularly for use cases such as demand forecasting, exception detection, or transportation optimization. Capability to design and maintain dashboard performance, front-end logic, and user experience in BI tools (Power BI, Tableau, etc. ) with a focus on usability for operational teams. Strong analytical mindset with a problem-solving orientation, capable of transforming operational needs into structured analytical use cases. Ability to collaborate across functions, engaging logistics, IT, planning, and customer service teams to co-develop effective data-driven solutions. Excellent communication skills (written and verbal) to explain technical findings to non-technical stakeholders and influence decision-making. Proactive and self-driven, with a focus on continuous improvement and a sense of ownership over data quality and system reliability. Agile mindset, comfortable working in fast-paced environments with evolving priorities and iterative solution design.