Lead Data Scientist

UKG•Weston, FL

About The Position

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.

Requirements

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.
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.
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.
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.
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.

Nice To Haves

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

Responsibilities

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.
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.
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.
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.
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.