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About This Role
Job Responsibilities
The Director, AI and Data Enablement will build and scale enterprise artificial intelligence (“AI”), digital capabilities, and a data operating and governance model that enables measurable business impact across Koppers.
Enterprise AI and Data Enablement Strategy
- Build the enterprise AI and data enablement roadmap, governance structure, and operating model, with a focus on business value and platform\-based capabilities.
- Identify, prioritize, and evaluate AI and analytics use cases across manufacturing, commercial, supply chain, finance, safety, legal, and corporate functions through a clear intake, governance, and value\-assessment process.
- Partner with senior leaders to shape the enterprise AI agenda, prioritize the highest\-value opportunities, and align investments with strategic business outcomes.
AI Enablement and Adoption
- Drive adoption of AI capabilities embedded in existing enterprise platforms, including Microsoft, ERP, EHS, CRM, supply chain, analytics, and related systems.
- Identify opportunities to leverage existing enterprise technology capabilities and vendor innovations before pursuing custom AI development.
- Establish and lead an AI Center of Enablement that supports business users with education, consultation, governance, and use\-case prioritization.
Business Partnership and Value Realization
- Partner with business and functional leaders to identify practical AI opportunities to improve productivity, safety, quality, cost, customer experience, and operational performance.
- Apply product management principles to develop reusable data products, AI\-enabled workflows, decision\-support tools and scalable capabilities to solve business problems.
- Develop communication, training, and change management strategies that drive successful adoption of AI technologies and data\-driven decision making.
- Measure and communicate outcomes including productivity improvements, cost savings, revenue opportunities, risk reduction, quality improvements, and operational efficiencies.
Data Governance and Enterprise Data Enablement
- Partner with business and technology leaders to strengthen enterprise data governance, data quality, master data management (MDM), and data stewardship practices.
- Establish standards for data ownership, quality, metadata management, lifecycle management, and governance processes.
- Support development of reusable data assets and enterprise data capabilities that improve scalability of AI and analytics initiatives.
- Collaborate with enterprise architecture, data platforms, and application teams to ensure data platforms support enterprise AI and analytics objectives.
- Support the development and execution of enterprise data strategies that improve accessibility, quality, consistency, and business value.
AI Governance, Risk, and Responsible AI
- Establish responsible AI standards, controls, and governance in partnership with Legal, Cybersecurity, Compliance, HR, Internal Audit, and business leadership.
- Maintain governance and approval processes for AI use cases, third\-party AI solutions, and emerging technologies.
- Monitor evolving AI regulations, industry trends, and emerging risks and incorporate them into enterprise governance practices.
Qualifications
- Bachelor’s degree in information systems, Computer Science, Data Science, Engineering, Business Analytics, Mathematics, Statistics, or a related field; Master’s degree preferred.
- Experience in manufacturing, chemicals, or other asset\-intensive industries preferred.
- Proficiency in technology, data, analytics, digital transformation, AI, enterprise applications, or related disciplines.
- Experience driving adoption of AI capabilities across Microsoft, ERP, CRM, EHS, supply chain, analytics, collaboration, and productivity solutions preferred.
- Expertise in enterprise AI adoption, data enablement, analytics, governance, digital transformation, or technology initiatives that delivered measurable business outcomes.
- Familiarity with Microsoft Azure AI, Microsoft Copilot, Microsoft Fabric, Power BI, Databricks, Snowflake, Oracle, Salesforce, SQL, Python, or similar technologies preferred.
- Strong program management, stakeholder management, and change management skills.
- Familiarity with enterprise data governance, master data management (MDM), and data quality programs preferred.
- Strong understanding of enterprise data management, data governance, AI governance, enterprise applications, security, privacy, and enterprise technology architecture.
- Strong knowledge of generative AI, agentic AI, predictive analytics, machine learning concepts, data visualization, and enterprise AI platforms.
- Familiarity with cloud architecture, APIs, data integration, enterprise architecture, and AI platform ecosystems preferred.
- Certification or training in AI, cloud platforms, project management, data governance, cybersecurity, or enterprise architecture preferred.
- Proven ability to communicate complex technical concepts to non\-technical audiences.
*Koppers Inc. and its subsidiaries are equal opportunity employers. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other category or characteristic protected by federal law, state or local law.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Koppers Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Koppers Inc. AI Hiring
Koppers Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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