Lead Data Scientist

Why UKG:

At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That's what we do.

We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you'll get flexibility that's real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters-and so do you.

Lead Data Scientist (P4)

Role Description
As a Lead Data Scientist (P4), you will lead the design, development, and delivery of advanced AI, machine learning, and Generative AI solutions that solve complex business problems and create measurable impact. This role is intended for a highly technical data scientist with a strong foundation in Computer Science or Software Engineering, in addition to deep expertise in machine learning, statistics, and experimental methods.

You will work closely with Data Engineers, ML Engineers, Software Engineers, Product Managers, and business stakeholders to build scalable, production-ready solutions. The ideal candidate is both a strong scientist and a disciplined builder: someone who can prototype intelligently, evaluate rigorously, and write high-quality, maintainable code using sound software engineering practices.

This role requires hands-on experience with LLMs, GenAI technologies, cloud AI services such as Google Cloud Platform Vertex AI, and modern AI development patterns including RAG, prompt orchestration, agent-based systems, model evaluation, and API-based integrations such as Gemini and ChatGPT/OpenAI APIs.

Key Responsibilities
Technical Leadership and Delivery
Lead the design, development, and implementation of advanced data science, machine learning, and Generative AI solutions for high-impact business use cases.
Own the end-to-end lifecycle of data science solutions, from problem framing and experimentation through productionization, monitoring, and iteration.
Translate ambiguous business challenges into clear technical approaches, experiments, prototypes, and scalable solutions.
Provide technical leadership across projects involving predictive modeling, NLP, LLMs, GenAI, and decision intelligence.
Software Engineering Excellence
Write clean, modular, well-tested, and maintainable production-quality code in Python and related technologies.
Establish and model strong engineering practices across the team, including:
Peer reviews
Pair programming / collaborative coding
Pull request discipline
Version control with Git/GitHub
Unit and integration testing
Code documentation
Reproducibility and traceability of experiments
Apply core software engineering principles such as abstraction, modularity, separation of concerns, code reuse, and performance optimization.
Partner effectively with software engineers and ML engineers to ensure data science solutions can be deployed, integrated, scaled, and supported in production environments.
Machine Learning and Generative AI
Design, train, evaluate, and optimize machine learning models using classical ML, deep learning, and NLP techniques.
Develop and implement GenAI solutions using LLMs, prompt engineering, embeddings, retrieval-augmented generation (RAG), agent frameworks, and model evaluation techniques.
Fine-tune or adapt foundation models for domain-specific use cases where appropriate.
Build prototypes and production-oriented solutions using tools and services such as Vertex AI, Gemini, OpenAI/ChatGPT APIs, model hosting services, vector stores, and orchestration frameworks.
Define robust evaluation methodologies for ML and GenAI systems, including offline metrics, human evaluation approaches, hallucination/risk assessment, and business outcome measurement.
Cloud and Platform Collaboration
Design and develop AI/ML solutions using Google Cloud Platform (Google Cloud Platform) services such as Vertex AI, BigQuery, GCS, and related cloud-native services.
Collaborate with Data Engineering and ML Engineering teams to build reliable data pipelines, feature preparation workflows, model deployment patterns, and monitoring strategies.
Ensure solutions are developed with scalability, cost-awareness, security, and operational sustainability in mind.
Contribute to best practices for MLOps, experiment management, model lifecycle governance, and responsible AI usage.
Mentorship and Cross-Functional Collaboration
Mentor other data scientists by raising the bar on scientific rigor, coding quality, review practices, and technical communication.
Lead by example in architecture discussions, design reviews, code reviews, and experimentation practices.
Collaborate with stakeholders across Product, Engineering, Sales, Marketing, Customer Success, and other business teams to identify opportunities for AI and ML to drive impact.
Communicate technical tradeoffs, findings, and recommendations clearly to both technical and non-technical audiences.
Contribute to the data science roadmap and help prioritize initiatives aligned with business strategy.

Qualifications
Education
PhD or Master's degree in Computer Science, Software Engineering, Machine Learning, Artificial Intelligence, Data Science, or a closely related quantitative field.
Strong preference for candidates with graduate-level work or research emphasis in Generative AI, NLP, LLMs, machine learning, or distributed/cloud computing.

Experience
7+ years of professional experience in Data Science, Applied Machine Learning, Machine Learning Engineering, or related roles.
Proven track record of delivering machine learning or AI solutions into production, not just research or notebook-based prototypes.
Demonstrated experience working in cross-functional teams with software engineers, data engineers, and ML engineers.
Experience mentoring other data scientists and influencing engineering and development standards across a team.

Technical Skills
Expert-level proficiency in Python and strong coding fundamentals grounded in software engineering best practices.
Strong knowledge of data structures, algorithms, object-oriented design, software design patterns, debugging, testing, and performance optimization.
Hands-on experience with Git/GitHub, pull request workflows, peer code review, branching strategies, and collaborative development practices.
Strong experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, and common Python data tooling.
Deep experience with LLMs, NLP, prompt engineering, embeddings, RAG, vector search, and agent-based application patterns.
Experience using Gemini, ChatGPT/OpenAI APIs, or similar foundation model platforms in real-world solutions.
Strong experience with Google Cloud Platform, particularly Vertex AI, BigQuery, and GCS; familiarity with cloud-native AI/ML workflows is strongly preferred.
Strong SQL skills and experience working with structured and unstructured data.
Familiarity with API design/integration, containerization, and production deployment patterns is preferred.
Scientific and Analytical Skills
Strong grounding in statistics, experimental design, hypothesis testing, model validation, and error analysis.
Ability to evaluate model behavior critically and design robust experiments for model comparison and iterative improvement.
Ability to balance scientific rigor with pragmatic business delivery.

Working Style and Leadership
Excellent communication skills with the ability to explain complex concepts clearly to technical and business audiences.
Strong collaboration skills and the ability to influence without authority across multiple teams.
Comfortable operating in Agile teams and contributing to planning, estimation, reviews, and iterative delivery.
Passion for technical excellence, continuous learning, and raising the quality bar for both science and engineering.

Preferred Qualifications
Experience building enterprise GenAI applications, internal copilots, knowledge assistants, summarization/classification systems, or workflow automation solutions.
Experience with agent orchestration frameworks, evaluation frameworks, guardrails, and responsible AI practices.
Familiarity with MLOps practices including model/version tracking, CI/CD, monitoring, and experiment reproducibility.
Experience in SaaS, enterprise software, customer experience, workforce technology, or product-led environments.
Google Cloud certifications such as:
Professional Machine Learning Engineer
Professional Data Engineer
Professional Cloud Architect
Professional Machine Learning Engineer
Professional Data Engineer
Professional Cloud Architect

Company Overview:

UKG is the Workforce Operating Platform that puts workforce understanding to work. With the world's largest collection of workforce insights, and people-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent, is unmatched. It's this expertise that equips our customers with the intelligence to solve any challenge in any industry - because great organizations know their workforce is their competitive edge. Learn more at ukg.com.

Equal Opportunity Employer

UKG is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, disability, religion, sex, age, national origin, veteran status, genetic information, and other legally protected categories.

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UKG participates in E-Verify. View the E-Verify posters here.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Disability Accommodation in the Application and Interview Process

For individuals with disabilities that need additional assistance at any point in the application and interview process, please email

The pay range for this position is $129,500.00 to $186,100.00 . The actual base pay offered may vary depending on skills, experience, job-related knowledge and work location. In addition to base pay, employees may be eligible to participate in a performance-based bonus plan and to receive restricted stock unit awards as part of total compensation. Learn more about UKG's benefits and rewards at