• Ingest, profile, and validate large-scale supply chain datasets sourced from multiple suppliers, LSPs, ERP systems, TMS platforms, and carrier feeds that originate at the consumption layer, establishing a clear baseline of data completeness, accuracy, and structural consistency. 

• Identify and systematically document data quality issues — including missing elements, erroneous values, structural inconsistencies, and misleading or conflicting data points — and develop root-cause analyses to distinguish systemic data failures from isolated anomalies.

• Design and propose data remediation strategies, including gap-filling methodologies, imputation techniques, and cleansing rule sets, then collaborate with internal & external stakeholders to validate and implement solutions.

• Monitor and validate incoming supplier and LSP data feeds on an ongoing basis following integration activation, profiling datasets against expected schemas, flagging structural deviations, completeness failures, and value-level anomalies, and escalating confirmed data integrity issues to the integration team with sufficient diagnostic documentation to support root-cause resolution.

• Develop and maintain analytical baselines across key value opportunity domains — including freight cost, carrier performance, lead time, and inventory utilization — delivering profiled datasets and baseline calculations in structured, consumable formats to Logistics Supply Chain Engineers and the Logistics Supply Chain Engineering Manager to support project-level baseline development.

• Develop and apply statistical and machine learning models to identify patterns, outliers, and predictive signals within supply chain data — including shipment lead times, supplier performance trends, inventory consumption rates, and freight cost anomalies.
• Delivery model outputs, predictive findings, and analytical baseline results in structured, consumable formats – including summary tables, annotated datasets, and visualization-ready data packages – enabling the Logistics Supply Chain Analyst to accurately incorporate Data Science outputs into operational dashboards and client reporting products.

• Contribute to QBR and MBR reporting packages, in coordination with the Logistics Supply Chain Engineering Manager and Logistics Supply Chain Analyst.

• Translate analytical findings into clear, actionable recommendations for operational stakeholders, presenting results in a format accessible to non-technical audiences including Control Tower personnel, Logistics Supply Chain Engineers, and client leadership.

• Partner with the Logistics Supply Chain Analyst to ensure data definitions, calculation methodologies, and KPI frameworks are grounded in clean, well-understood source data.

• Maintain documentation of data sources, transformation logic, model assumptions, and known data limitations, ensuring analytical outputs are reproducible and auditable.