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About This Role
Job descriptionCompany and benefits
Job IDLEADD017950
Employment TypeRegular
Work Styleon\-site
LocationWeston,FL,United States
TravelUp to 25%
RoleLead 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 GCP 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 (GCP) 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 GCP, 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.
View The EEO Know Your Rights poster
UKG participates in E\-Verify.
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 UKGCareers@ukg.com.
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 https://www.ukg.com/about\-us/careers/benefits
NOTICE ON HIRING SCAMS
UKG will never ask you for a copy of your driver’s license, social security card, or passport during a job inter
ABOUT OUR JOB DESCRIPTIONS
All job descriptions are written to accurately reflect the open job and include general work responsibilities. They do not present a comprehensive, detailed inventory of all duties, responsibilities, and qualifications required for the job. Management reserves the right to revise the job or require that other or different tasks be performed if or when circumstances change.
Salary Context
This $129K-$186K range is below the median for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At UKG, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 23% below the category median. Disclosed range: $129K to $186K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
UKG AI Hiring
UKG has 3 open AI roles right now. They're hiring across Data Scientist, AI Architect, AI Product Manager. Positions span Weston, FL, US, San Francisco, CA, US, Lowell, MA, US. Compensation range: $186K - $335K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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