Sr. Data Scientist- Eng

Technical strategy & vision

Define and own the long‑term Data Science technical strategy — the multi‑year roadmap for platform capabilities, tooling investments, and methodology evolution. Anticipate challenges and opportunities over a multi‑year horizon and build stakeholder alignment for strategic initiatives. Align Data Science capabilities with broader business strategy.

Design coherent, multi‑team architecture that enables Data Science work across the organization. Create architectural patterns adopted broadly, anticipating scale and evolution needs to minimize rework as the organization grows. Review and guide critical architecture decisions across the portfolio.

Identify portfolio‑wide improvement opportunities. Execute large‑scale projects spanning multiple teams and value streams with year‑long horizons. Drive step‑change improvements in DS capabilities. Own decisions with area‑wide impact and create scalable solutions that reduce toil across the organization.

Make org‑level decisions balancing multiple competing interests. Avoid locally optimal decisions that create downstream costs. Create frameworks that enable others to make consistently good decisions. Evaluate major technology investments — build vs. buy, vendor selection, and platform commitments.

Define org‑wide standards for AI and ML development — selecting frameworks and architectural patterns teams should follow. Evaluate emerging AI/ML frameworks across the industry and drive adoption of high‑value tools. Create evaluation frameworks and best practices for agentic systems. Champion experimentation with novel AI architectures.

Develop a deep understanding of UKG's business model and competitive strategy. Translate business needs into DS investments, identifying which ML capabilities would most accelerate business goals. Influence business decisions with DS insights at the executive level. Influence roadmaps across multiple teams for measurable business impact.

Mentor senior ICs; spread knowledge across teams. Be active in senior hiring and build the DS talent pipeline. Shape org‑wide learning and development strategy. Model continuous learning by seeking feedback from diverse sources including senior leaders and peers at peer companies.

Remain resilient through significant organizational change and help the organization navigate uncertainty. Push boundaries with breakthrough approaches. Connect emerging technologies to business opportunities and create space for innovation across teams.

PhD in a quantitative field plus 5+ years of industry experience, or Master’s degree plus 10+ years of experience in data science, machine learning, or a closely related discipline.

Expert‑level fluency in multiple DS specialty areas — generative AI, agentic AI systems, LLMs and foundation models, deep learning, time‑series forecasting, regression, classification, or NLP.

Demonstrated ability to own and drive multi‑year technical strategy spanning multiple teams and product areas, with measurable impact across value streams.

Proven ability to influence roadmaps, platform decisions, and cross‑functional teams — including product, engineering, and senior leadership — without direct managerial authority.

Advanced programming skills in Python; fluency in ML frameworks (PyTorch, Tensor Flow), data tools (Pandas, Num Py, SQL), and cloud platforms such as GCP / Agent Platform and Big Query, or equivalent.

Experience ensuring data quality and governance at organizational scale; influencing data strategy across multiple teams.

Exceptional ability to communicate architectural tradeoffs, multi‑year strategy, and quantitative results to both technical peers and executive stakeholders.

Experience with model fine‑tuning (SLMs, RLHF, distillation), multi‑modal architectures, or advanced retrieval‑augmented generation (RAG) systems.

Advanced knowledge of GCP technologies:
Agent Platform, Big Query, GKE, Dataflow, or equivalent infrastructure for large‑scale ML workloads.

Hands‑on experience designing and product ionizing multi‑agent systems using agentic frameworks (e.g., Lang Graph, Model Context Protocol) and defining best practices for their org‑wide adoption.

Experience across multiple HCM product domains (e.g., Workforce Management, Talent & Recruiting, Pay & Compensation, People ) with insight into how DS drives value differently across each.

Experience setting org‑wide coding standards and creating foundational libraries and frameworks.

Familiarity with responsible AI principles: fairness, interpretability, privacy‑preserving ML, and model risk management at scale.