Director Data Science

Job: Director Data Science

Location: Remote with 25% travel to Pittsburgh, PA

Duration: 12+ months contract

Job Description

Responsible for defining and leading Client s enterprise Data Science and Artificial Intelligence strategy, capabilities, and delivery. Provide enterprise leadership in identifying, developing, and scaling advanced analytics, machine learning, Generative AI, and AI-enabled solutions that drive measurable business value across Sales, Supply Chain, Pricing, Operations, Finance, Digital, and other business functions.

This role establishes the Data Science and AI operating model, technology and solution framework, development standards, and roadmap required to turn enterprise data into actionable intelligence and scalable AI capabilities. The Director will partner closely with business and technology leaders to identify high-value opportunities, build production-ready AI solutions, and drive adoption across the organization.

The role will build and lead a high-performing team of data scientists, ML/AI engineers, and analytics professionals while establishing strong practices around model development, MLOps, Responsible AI, governance, performance measurement, and continuous improvement.

Responsibilities

1. Define and execute Client s enterprise Data Science and AI strategy and roadmap, aligned with business priorities and enterprise technology strategy.
2. Build and lead enterprise capabilities across machine learning, predictive analytics, optimization, Generative AI, and intelligent automation.
3. Partner with business stakeholders across Sales, Supply Chain, Pricing, Inventory, Finance, Digital, Operations, and IT to identify and prioritize high-value Data Science and AI opportunities.
4. Establish a structured AI use-case portfolio and prioritization process based on business value, feasibility, data readiness, scalability, and risk.
5. Lead the end-to-end lifecycle of AI/ML products from business problem definition, data exploration and experimentation through model development, production deployment, monitoring, and continuous improvement.
6. Build and lead a team of Data Scientists, ML/AI Engineers, AI Architects, and Analytics professionals, including hiring, mentoring, career development, and establishing technical standards.
7. Establish enterprise MLOps and LLMOps practices to enable repeatable model development, deployment, monitoring, retraining, versioning, and lifecycle management.
8. Drive the adoption of Generative AI and agentic AI capabilities, including enterprise copilots, knowledge assistants, intelligent workflow automation, and AI-enabled decision support.
9. Collaborate with Data Architecture, Data Engineering, Master Data Management, Security, Enterprise Architecture, and application teams to ensure availability of trusted, secure, high-quality data for AI solutions.
10. Establish Responsible AI standards covering model governance, security, privacy, explainability, bias, human oversight, and appropriate use of enterprise and customer data.
11. Evaluate and recommend modern AI technologies and platforms, including capabilities across Microsoft Azure, Databricks, Azure AI, Azure Machine Learning, and modern Generative AI/LLM platforms.
12. Establish and report Data Science and AI KPIs, including business value realized, adoption, model performance, time-to-production, operational impact, and ROI.
13. Develop repeatable solution patterns and reusable AI assets that enable successful solutions to scale across business units, geographies, and functions.
14. Partner with senior business and technology leadership to communicate AI opportunities, investment requirements, risks, and business outcomes.
15. Build an enterprise culture of data-driven decision making and responsible AI adoption, helping business teams understand how advanced analytics and AI can improve business performance.

Qualifications

1. Bachelor s Degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Operations Research, Engineering, or a related discipline required; Master s Degree preferred.
2. 10+ years of experience in Data Science, Advanced Analytics, Machine Learning, AI, or related technology disciplines, including significant leadership responsibility.
3. 5+ years of experience leading Data Science/AI teams, including building teams, hiring technical talent, mentoring leaders, and developing organizational capabilities.
4. Demonstrated experience creating and executing an enterprise Data Science and AI strategy and roadmap.
5. Strong practical knowledge of machine learning, statistical modeling, forecasting, optimization, recommendation systems, NLP, Generative AI, and Large Language Models (LLMs).
6. Experience taking AI/ML solutions from experimentation and proof-of-concept through enterprise-scale production deployment.
7. Strong understanding of modern MLOps/LLMOps, including model deployment, monitoring, versioning, evaluation, retraining, observability, and lifecycle management.
8. Experience with enterprise data and AI platforms such as Databricks, Microsoft Azure, Azure Machine Learning, Azure AI, Python, SQL, Spark, and related cloud/data technologies.
9. Experience designing and implementing Generative AI solutions, including Retrieval-Augmented Generation (RAG), enterprise knowledge solutions, copilots, AI agents, and LLM-based applications.
10. Strong understanding of Responsible AI, AI governance, data privacy, model risk, cybersecurity, and enterprise AI controls.
11. Experience applying Data Science and AI to business domains such as supply chain, demand forecasting, inventory optimization, pricing, sales effectiveness, customer analytics, product intelligence, and operational efficiency strongly preferred.
12. Ability to translate complex business problems into analytical and AI solutions with measurable financial and operational outcomes.
13. Strong executive communication and presentation skills with the ability to communicate effectively with both technical and non-technical audiences.
14. Demonstrated ability to lead multiple complex initiatives simultaneously while working across business functions and technology organizations.
15. Strong strategic planning, problem-solving, organizational, and stakeholder-management skills.
16. Experience managing strategic technology partners, consulting organizations, and AI/data platform vendors.
17. Exceptional ability to operate under uncertainty, evaluate emerging technology, and balance innovation with enterprise scalability, security, and governance.
18. Ability to travel up to 25%.

What I would emphasize when recruiting for this role

Given the Client s environment, we would not position this as a traditional Head Data Scientist role. The ideal candidate should be roughly 50% enterprise AI/Data Science leader, 30% business transformation leader, and 20% hands-on technical authority.

The strongest candidates should have three differentiators:

• Built an AI/Data Science organization, not simply inherited or managed one.
• Productionized AI at enterprise scale, particularly Databricks/Azure, ML/GenAI, MLOps and governance, rather than primarily running POCs.
• Strong supply-chain/distribution experience, especially forecasting, inventory, pricing, sales/customer analytics and optimization. That will make the profile particularly relevant to Client s business.