Lead Data Scientist

Key Responsibilities

• Drive AI Innovation that Delivers Customer-Focused Impact
• Spearhead development of Agentic architectures, including multi-agent patterns, deliver innovative AI solutions.
• Identify high-impact opportunities in demand planning, inventory management, financial forecasting, and document processing.
• Continuously Improve Insights Across Key Business Domains
• Provide deep data science expertise and thought leadership in Supply Chain and Finance functions, understanding domain-specific challenges and KPIs (e.g., forecast accuracy, working capital, cost variances).
• Innovate Predictive & Optimization Solutions for Growth
• Develop and deploy predictive models and optimization algorithms that address critical ISC and other domain problems.
• Own-It, End-to-End Projects
• Lead end-to-end data science projects – defining concrete opportunities from vague problem statements, data extraction and exploration through model training, validation, and deployment.
• Work hands-on with large, complex datasets (e.g., ERP data, supply chain data, financial ledgers) to extract insights.
• Maintain high standards of data quality and model performance, and implement MLOps best practices for versioning, monitoring, and continuous improvement of models in production.
• Advance Together Through Strong Cross-Functional Partnerships
• Partner closely with Supply Chain analysts, Logistics managers, Finance controllers, and IT data teams to gather requirements and implement data-driven solutions.
• Translate complex analytical findings into actionable business insights (e.g., identifying drivers of inventory write-offs or cost overruns) and communicating these insights to non-technical stakeholders to inform decision-making.

Qualifications

• Advanced degree (Bachelor’s or above) in Data Science, Statistics, Computer Science, Operations Research, or related field.
• 8+ years of experience in data science or advanced analytics roles, including deploying solutions that drive measurable value (ideally in supply chain and/or finance contexts).
• Expert-level programming in Python and SQL; proficiency with libraries/frameworks such as pandas, scikit-learn, TensorFlow/PyTorch; experience with Databricks or Azure Cloud ML services.
• Strong grasp of statistical modeling, machine learning algorithms, and data mining techniques for time-series forecasting and classification/regression.
• Solid understanding of supply chain (demand forecasting, S&OP, inventory optimization) and finance (budgeting/planning, reporting, cost analysis) concepts.
• Excellent problem-solving and ability to break ambiguous problems into actionable data questions; strong communication skills to influence business leaders.
• Experience leading data science projects and/or teams; strong collaboration skills across interdisciplinary stakeholders; comfort presenting to executive audiences.
• Familiarity with enterprise data environments and tools (e.g., SAP/ERP, supply chain management systems, financial databases) and integrating solutions into production workflows (APIs, dashboards, business applications).
• Passion for innovation (competitions, publications, patents) and ability to evaluate and introduce new tools/technologies when they add value.
• Results-driven mindset with clear objectives (e.g., reducing forecast error by X% or accelerating financial close by Y days) and tracking against outcomes.
• Commitment to continuous learning, including emerging AI trends (Generative AI and agentic AI) and responsible application in business processes.