Job Title

TalentXM is seeking an AI/ML Supply Chain Data Scientist to apply machine learning and advanced analytics to complex supply chain challenges. This role leverages data to forecast demand, optimize routes and inventory, improve network efficiency, and generate predictive insights from large and diverse operational datasets, including sales, production, logistics, and IoT data.The ideal candidate combines strong data science and machine learning capabilities with practical supply chain knowledge and the ability to translate analytical outputs into measurable business impact.Key responsibilitiesAnalyze large datasets related to supply chain performance, including historical sales, inventory levels, shipment transit times, and operational data, to identify patterns, trends, and outliers. Develop and refine forecasting models for demand planning, lead times, and supply using time-series analysis, regression, and other machine learning techniques. Design and implement optimization algorithms for routing, network design, inventory placement, and resource allocation to improve service levels and reduce cost. Apply predictive analytics to anticipate supply chain risks such as delays, stockouts, and quality issues. Collaborate with cross-functional teams across logistics, production, procurement, and operations to ensure analytical models reflect business realities and constraints. Build and support data pipelines and analytical tools that integrate data from ERP, WMS, IoT, and external sources for preprocessing, feature engineering, and model development. Develop dashboards, applications, or decision-support tools that deliver insights to stakeholders in real time. Validate and back-test models, and measure the business impact of data science initiatives through clear performance metrics. Present findings and recommendations clearly to non-technical stakeholders, translating analytical results into actionable business decisions. Stay current on emerging AI/ML methods and evaluate their applicability to supply chain use cases such as demand sensing and dynamic routing. Support broader digital transformation initiatives through knowledge sharing, process improvement, and collaboration with technical and business teams. Required qualificationsMinimum of 5 years of hands-on experience in data science, machine learning, or predictive analytics applied to supply chain, logistics, manufacturing, or related operational environments.Strong proficiency in Python and SQL, with hands-on experience in data cleaning, feature engineering, statistical analysis, and end-to-end model development. Experience building forecasting and time-series models for business or operational planning. Familiarity with optimization techniques such as linear programming, operations research, and algorithm design. Solid understanding of supply chain concepts including demand planning, logistics optimization, inventory management, and supply chain modeling. Strong communication skills with the ability to explain technical findings to business stakeholders and translate them into practical recommendations. Strong analytical and problem-solving skills with a focus on measurable business outcomes. Preferred qualificationsExperience with TensorFlow or PyTorch for advanced machine learning applications. Experience with R for statistical modeling and analysis. Experience with visualization tools such as Tableau or Power BI. Familiarity with Hadoop, Spark, or cloud ML platforms such as AWS SageMaker or Azure ML. Experience working with IoT data, ERP systems, or WMS platforms in supply chain environments. Exposure to custom algorithm development for optimization, routing, or network analysis. Experience working cross-functionally with logistics, procurement, manufacturing, and operations teams.