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.

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