Procurement Data Science & Analytics Manager

Huntsman Corporation•Houston, TX

23h

About The Position

Procurement Data Science & Analytics Manager Huntsman is seeking a Procurement Data Science & Analytics Manager supporting the Global Procurement organization located in The Woodlands, Texas. This position will lead the analytics vision across Procurement and adjacent operations and partner closely with Procurement leadership, Supply Chain, Finance, Commercial, and IT stakeholders. Job Scope The Procurement Data Science & Analytics Manager is responsible for defining and executing Huntsman’s Procurement analytics strategy, including advanced analytics, AI/ML enablement, data governance, and enterprise dashboarding. This role leads a team of analysts, owns the analytics roadmap, and translates complex data into actionable insights that improve margin, cost visibility, supply resilience, and operational performance. In summary, as the Procurement Data Science & Analytics Manager, you will: Lead, coach, and develop a team of data analysts, ensuring strong execution, prioritization, PDP management, and career development. Set technical standards for analytics development, data engineering practices, automation, and quality. Own Procurement’s data strategy, including data models, hierarchies, governance, and audit compliance. Oversee data sourcing from SAP, SAP HANA, Databricks, and external systems, ensuring data quality, lineage, and refresh reliability. Establish standards for master data, metadata, and documentation to support scalability and reuse. Design and lead development of predictive and advanced analytics models, including forecasting accuracy improvement, PPV drivers, cost indices, contract leakage detection, supplier-risk modeling, and should-cost analytics. Architect and deploy AI-enabled tools and automation, including Copilot Studio, ML pipelines, text mining, anomaly detection, and automated data flows. Govern the full analytics and model lifecycle (MLOps, versioning, validation, monitoring, bias checks, and integration into Power BI and workflows). Partner with Procurement leadership to frame business questions and translate analytics into clear, actionable recommendations for executive decision-making. Drive insights that support cost savings, supplier performance, supply resilience, and working-capital improvements. Collaborate with Finance, Supply Chain, Operations, and Commercial teams on integrated analytics and dashboards. Own and continuously improve the full portfolio of Procurement dashboards, including spend, supplier performance, OTIF, PPV, pricing, operations, capex, commercial support, and forward-looking indicators. Improve dashboard architecture, reduce refresh failures, and streamline leadership consumption. Lead continuous improvement of the Databricks analytics environment. Communicate complex analytics clearly and concisely to VP- and C-suite-level audiences. Provide proactive visibility into risks, cost trends, and key performance drivers.

Requirements

7–10+ years of experience in data analytics, data science, FP&A analytics, supply chain analytics, or procurement analytics (chemical industry experience preferred).
Demonstrated experience leading and developing a technical analytics or data science team.
Strong proficiency in SAP, SAP HANA, SQL, Power BI (advanced), and Databricks.
Hands-on experience developing and deploying machine learning models, AI automation, and Copilot-enabled solutions.
Strong business acumen with the ability to influence senior and executive stakeholders.
Strong leadership, coaching, and team development skills.
Ability to translate complex technical analytics into business-ready insights and recommendations.
Strategic thinking with a continuous improvement mindset.
Strong stakeholder management and cross-functional collaboration skills.
High level of accountability, prioritization, and execution discipline.
Comfort operating in a fast-paced, data-driven, and evolving environment.

Nice To Haves

Experience building business-ready AI agents using Copilot Studio.
Knowledge of procurement analytics methodologies such as should-costing, PPV analysis, and index tracking.
Familiarity with Celonis, Alteryx, Automation Anywhere, or similar analytics and automation platforms.
Prior experience partnering directly with Procurement and Operations leadership teams.

Responsibilities

Lead, coach, and develop a team of data analysts, ensuring strong execution, prioritization, PDP management, and career development.
Set technical standards for analytics development, data engineering practices, automation, and quality.
Own Procurement’s data strategy, including data models, hierarchies, governance, and audit compliance.
Oversee data sourcing from SAP, SAP HANA, Databricks, and external systems, ensuring data quality, lineage, and refresh reliability.
Establish standards for master data, metadata, and documentation to support scalability and reuse.
Design and lead development of predictive and advanced analytics models, including forecasting accuracy improvement, PPV drivers, cost indices, contract leakage detection, supplier-risk modeling, and should-cost analytics.
Architect and deploy AI-enabled tools and automation, including Copilot Studio, ML pipelines, text mining, anomaly detection, and automated data flows.
Govern the full analytics and model lifecycle (MLOps, versioning, validation, monitoring, bias checks, and integration into Power BI and workflows).
Partner with Procurement leadership to frame business questions and translate analytics into clear, actionable recommendations for executive decision-making.
Drive insights that support cost savings, supplier performance, supply resilience, and working-capital improvements.
Collaborate with Finance, Supply Chain, Operations, and Commercial teams on integrated analytics and dashboards.
Own and continuously improve the full portfolio of Procurement dashboards, including spend, supplier performance, OTIF, PPV, pricing, operations, capex, commercial support, and forward-looking indicators.
Improve dashboard architecture, reduce refresh failures, and streamline leadership consumption.
Lead continuous improvement of the Databricks analytics environment.
Communicate complex analytics clearly and concisely to VP- and C-suite-level audiences.
Provide proactive visibility into risks, cost trends, and key performance drivers.