Principal Data Scientist- Eng

Company-wide technical strategy: Architecture & infrastructure investment: Architect DS systems at company scale, creating designs that work across all product areas and use cases. Create architectural patterns that enable long-term evolution. Evaluate major infrastructure investments, providing technical perspective on platform choices that will shape DS work for years. Review critical design decisions across the organization. Business-critical results & ownership: Identify company-wide opportunities for DS impact. Execute initiatives with business-critical outcomes that create competitive advantage. Own decisions with company-wide implications and be accountable for DS strategy execution. Create organizational capabilities — frameworks, platforms, and standards — that outlast individual projects. Strategic decision-making & executive influence: Make strategic decisions with long-term implications, balancing multiple organizational priorities simultaneously. Build decision frameworks adopted broadly across the DS org. Represent DS in strategic planning and influence business decisions at the executive level, providing analysis and perspective that shapes the company's strategic choices. AI & agentic systems leadership: Define the company-wide agent development strategy, positioning UKG to leverage agentic AI for competitive advantage. Evaluate transformational opportunities in AI/ML frameworks and foundation models; advise on major vendor relationships. Set the bar for agent development excellence org-wide. Ensure agent governance at enterprise scale — safety, reliability, compliance, and responsible AI. Represent UKG externally on agent development. Innovation agenda & organizational resilience: Define the DS innovation agenda. Connect emerging capabilities to business transformation opportunities. Create an environment where breakthrough work is possible across teams. Guide the organization through major transitions and model resilience at senior level. Evaluate emerging techniques and frameworks for strategic fit — recognized externally as a domain expert. Talent strategy & leadership development: Build the DS leadership bench by mentoring senior ICs and managers. Shape the DS career framework and define the company-wide learning vision for data science. Drive hiring strategy and pipeline development. Model lifelong learning and continuously evolve expertise through new challenges. Business acumen & competitive positioning: Develop deep understanding of industry dynamics and competitive landscape — how UKG wins in the market and how competitors are investing in ML. Translate business strategy to DS strategy and influence data strategy company-wide. Ensure DS data needs are met at enterprise scale and that governance meets enterprise requirements. PhD in a quantitative field plus 8+ years of industry experience, or Master's degree plus 12+ years of experience in data science, machine learning, or a closely related discipline, with a demonstrated trajectory of increasing organizational scope and impact. Recognized technical authority in the broader data science community, with expertise across multiple AI/ML specialty areas. Track record of evaluating emerging techniques for strategic fit and driving their adoption across an organization. Proven ability to set and execute company-wide DS technical strategy, with accountability for outcomes that span multiple product areas and deliver business-critical results. Demonstrated ability to influence executive-level decisions on products, technology investments, and business strategy — and to represent data science as a strategic function in senior planning forums. Experience designing and governing DS systems at enterprise scale — including MLOps, model governance, data quality, responsible AI, and compliance requirements across a large organization. Strong command of ML frameworks, cloud platforms (GCP / Agent Platform, BigQuery, or equivalent), and the full modern AI/ML toolchain. Ability to set company-wide standards for code quality, automation infrastructure, and documentation. Demonstrated history of personally building and delivering production ML and AI models with hands-on depth across the full modeling lifecycle: problem framing, feature engineering, model development, evaluation, and post-deployment monitoring. Experience building, mentoring, and developing senior technical talent including staff-level ICs and engineering managers. Demonstrated influence on career frameworks and org-wide learning programs. Exceptional communication skills at the executive level — ability to articulate complex technical strategy, competitive tradeoffs, and long-term DS investments in terms that drive organizational alignment and business decisions. Experience advising on major vendor relationships, platform commitments, and build vs. buy decisions for AI/ML infrastructure at enterprise scale. Experience designing and governing enterprise-scale agentic AI systems, including safety, reliability, and compliance frameworks for multi-agent architectures. Deep familiarity with the HCM industry — understanding how Workforce Management, Talent, and Pay create differentiated value — and how ML capabilities translate to competitive advantage in that market. Experience shaping an organization's DS career framework, hiring strategy, and technical learning culture at scale. Advanced expertise in responsible AI at enterprise scale: fairness, interpretability, privacy-preserving ML, model risk management, and regulatory compliance.