Senior Director, Data & AI

Unisys•Richmond, PA

About The Position

The Senior Director, Data & AI defines the global Data & AI vision, strategy, and multi-year roadmap in support of company growth, profitability, and client outcomes. The role sets technology direction across data platforms, engineering, integration, analytics, machine learning, generative AI, and agentic AI, while making modern, well-governed data estates a foundation for AI adoption. It tracks market, partner, regulatory, and technology developments; sponsors innovation; and moves proven approaches into production and repeatable practices. The role owns the delivery and operational model for Data & AI engagements, from design through production. It sets standards for quality, security, cost, and timelines; establishes reusable methods and architectures; and oversees production operations such as MLOps, monitoring, support, and continuous improvement. It also defines architecture, data governance, and responsible-AI principles, partnering with Security, Legal, and Compliance on risk, intellectual property, and data protection. As a senior client advisor and business leader, the Senior Director builds executive relationships, supports strategic pursuits, manages partner relationships, contributes to commercial models, and represents the company as a Data & AI thought leader. The role owns organizational revenue, margin, utilization, budget, capacity, hiring, and risk targets, and leads a high-performing global team across data engineering, data science, AI engineering, architecture, and consulting. It develops talent, leaders, career paths, succession plans, skills, and certifications to support innovation and growth.

Requirements

15 or more years of experience in data, analytics, AI, or technology consulting.
At least 8 years leading large, global technical teams.
Deep technical knowledge across modern data platforms, data engineering, analytics, machine learning, generative AI, and agentic AI, with solutions taken from design into production.
Hands-on understanding of cloud data platforms and AI services from major hyperscalers, such as AWS, Microsoft Azure, and Google Cloud.
Understanding of data governance, security, and responsible-AI practices.
Track record of delivering large, complex Data & AI programs on time, on budget, and to quality standards.
Experience owning financial targets such as revenue, margin, utilization, and budget.
Strong client-facing skills, including advising senior executives and supporting pursuits for large enterprise or public-sector clients.
Ability to work across Sales, Delivery, Engineering, Finance, Legal, and partner organizations and influence without direct authority.
Executive-level communication skills and ability to explain complex topics simply.
Bachelor’s degree in computer science, data science, engineering, or a related field.
Applicant should be eligible for any required U.S. Government authorizations if the role requires access to export-controlled commodities and technology.

Nice To Haves

Master’s degree.
Recent data or AI certifications.

Responsibilities

Define the global Data & AI vision, strategy, and multi-year roadmap aligned with company growth, profitability, and client outcomes.
Set technology direction across data platforms, engineering, integration, analytics, machine learning, generative AI, and agentic AI.
Guide clients and internal teams toward modern, well-governed data estates as a foundation for AI adoption.
Track market trends, hyperscaler and partner roadmaps, regulation, and emerging technologies, and translate them into investment priorities.
Sponsor pilots, proofs of concept, and accelerators, and move proven ideas into production and repeatable practices.
Own delivery of Data & AI engagements from design through production, including data modernization, data engineering, analytics, machine learning, generative AI, and agentic AI solutions.
Set delivery standards for quality, security, cost, and timelines, and intervene in high-risk or strategic engagements.
Establish reusable methods, accelerators, reference architectures, and tooling to improve delivery speed and consistency.
Build and run the production operating model for data and AI, including MLOps, monitoring, support, and continuous improvement.
Coordinate with Delivery, Engineering, and Forward Deployed teams to share resources, skills, and lessons learned.
Set architecture principles for secure, scalable, cost-efficient data and AI platforms across cloud and hybrid environments.
Establish data governance practices covering quality, lineage, privacy, security, and regulatory compliance.
Define responsible-AI policies and testing standards covering bias, explainability, safety, and human oversight, and apply them before client use.
Partner with Security, Legal, and Compliance on risk, intellectual property, and data-protection requirements for each market.
Advise client executives on data and AI strategy and develop long-term relationships that generate repeat and expanded work.
Support Sales and Presales on strategic pursuits through solution shaping, oral presentations, workshops, and delivery-risk support.
Build and manage strategic relationships with hyperscalers, data and AI platform vendors, and alliance partners.
Contribute to pricing, estimation, and commercial models in partnership with Finance and Pricing.
Represent the company at industry events and with analysts as a Data & AI thought leader.
Own and report revenue, margin, utilization, and budget targets for the Data & AI organization.
Plan capacity and hiring against demand, balancing employees, contractors, and partners.
Manage delivery, financial, and security risk with clear escalation paths.
Set decision rights and ways of working with Sales, Delivery, Engineering, Architecture, Finance, Marketing, and shared functions.
Build and lead a high-performing global team of data engineers, data scientists, AI engineers, architects, and consultants, including managers and senior technical leaders.
Hire, develop, and retain talent through career paths, succession plans, and a culture of continuous learning.
Set skills and certification goals across data platforms, machine learning, generative AI, and agentic AI.
Coach leaders and teams on technical excellence, client engagement, and executive communication.
Align organizational structure, skills, and ways of working to support rapid innovation and growth.