Data Scientist Segment Lead

Reference #: JR262521

As the Data Science Practice Lead within PPG's Digital Organization, this role is responsible for leading and shaping PPG's enterprise Data Science capability, with a primary emphasis on materials science, formulation, and processcentric applied data science.

The individual will bring deep hands-on expertise in materials and chemical data science-including formulation optimization, experimental design, structure-property modeling, and scaleup analytics-while also providing technical leadership and architectural guidance for data science applications across manufacturing, supply chain, commercial, and customer-facing functions.

This role bridges science, data, and digital product delivery, ensuring that advanced analytics and AI are best suited to the underlying physical, chemical, and operational realities of PPG's businesses.

This role will sit at our headquarters in Pittsburgh, PA on a hybrid schedule. If not local - we will provide relocation benefits.

Key Responsibilities

Practice & Technical Leadership (Primary) Lead the PPG Data Science practice, setting technical standards, methodologies, and best practices for applied data science across the enterprise. Serve as the senior technical authority for materialssciencedriven data science, including formulation development, DOEbased modeling, hybrid physics/ML approaches, and experimental data analytics. Define and evolve domainappropriate modeling approaches (e.g., surrogate models, Bayesian optimization, multiobjective optimization, structure-property relationships) aligned to scientific workflows in S&T and formulation teams. Model Development & Deployment Lead the design, development, and deployment of advanced AI/ML and statistical models that solve complex, realworld problems rooted in chemistry, materials, and processes. Guide teams from problem framing through deployment, ensuring models are scientifically sound, interpretable where required, and operationally robust. Establish clear model performance, adoption, and valuecapture metrics tied to business and scientific outcomes. CrossFunctional & Enterprise Enablement Partner closely with S&T, formulation, manufacturing, quality, supply chain, commercial, and digital product teams to ensure data science solutions are embedded into real workflows. Act as a technical translator across domains-helping nonscience functions understand how advanced data science applies to their problems and helping scientists leverage modern analytics effectively. Ensure consistency and reuse of data science assets, patterns, and platforms across functions. Data & Analytics Strategy Define and influence data strategies and architectures that support scientific and operational analytics, including experimental data, lab systems, process data, and enterprise data platforms. Ensure data quality, lineage, and integrity standards appropriate for scientific decisionmaking and regulated environments. Collaborate with data engineering and platform teams to ensure scalable, productionready analytics infrastructure. Monitoring, Governance & Continuous Improvement Establish best practices for model monitoring, validation, lifecycle management, and continuous improvement, particularly for models supporting scientific and operational decisions. Promote responsible, transparent, and fitforpurpose use of AI/ML across domains. Leadership & People Management Lead, mentor, and develop senior and midlevel data scientists, with an emphasis on building domainaware data science talent. Drive hiring, onboarding, and capability development for the Data Science practice, balancing deep domain expertise with modern ML skills. Foster a culture of scientific rigor, experimentation, collaboration, and practical impact. Executive Communication & Influence Communicate complex analytical concepts, results, and tradeoffs clearly to senior technical and business stakeholders. Influence strategy by articulating where advanced data science is best suited to deliver differentiation and measurable value for PPG.

Qualifications

5+ years of experience in data science, applied analytics, or related fields, with a bachelor's degree or higher in data science, materials science, chemical engineering, statistics, physics, or a related discipline (or equivalent experience). Deep, demonstrated expertise in materials or chemistryadjacent applied data science, including experimental data analysis, DOE, formulation or process modeling, and optimization. Proven experience leading and deploying advanced analytics and AI/ML solutions into production environments. Strong foundation in statistical modeling, machine learning, and modern data science tooling. Demonstrated ability to lead teams, mentor senior talent, and set technical direction. Preferred: Advanced degree (Master's or PhD) in Materials Science, Chemical Engineering, Chemistry, Applied Statistics, or related field. Experience with hybrid physicsbased + ML models, Bayesian methods, or scientific computing. Experience in applying data science across multiple enterprise functions Strong executive communication skills and experience influencing senior leaders. #LI-Hybrid

About us:

Here at PPG we make it happen, and we seek candidates of the highest integrity and professionalism who share our values, with the commitment and drive to strive today to do better than yesterday - everyday.

PPG: WE PROTECT AND BEAUTIFY THE WORLD

Through leadership in innovation, sustainability and color, PPG helps customers in industrial, transportation, consumer products, and construction markets and aftermarkets to enhance more surfaces in more ways than does any other company. To learn more, visit and follow @ PPG on Twitter.

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Our employee benefits programs are designed to support the health and well-being of our employees. Any insurance coverages and benefits will be in accordance with the terms and conditions of the... For full info follow application link.

PPG provides equal opportunity to all candidates and employees. We offer an oppo