Scientifique Principale des Données-Ingénieur - Staff Data Scientist - Eng
About The Position
At UKG, our AI Data Science team partners with our AI and domain product and engineering organizations to design and deliver production-grade Data Science and AI capabilities embedded directly into our workforce management and human resource management solutions. We leverage large-scale workforce data and apply advanced machine learning, analytics, and cutting-edge AI techniques to build intelligent systems that power real customer outcomes. As a Data Scientist, you will design, develop, and deliver innovative data science solutions spanning the UKG product domain, with a strong machine learning engineering bent. You will own projects from problem definition through production deployment, building scalable ML systems that drive measurable business impact for millions of UKG customers. This principal IC role combines deep modeling expertise with the engineering rigor required to train, serve, and operate models at scale–and pairs both with the technical leadership and mentorship needed to drive UKG’s AI innovation roadmap. The ideal candidate will work from our Montreal office three days a week.
Requirements
- Education and experience in a quantitative or engineering field. PhD plus 3 years of experience, or master’s degree plus 5 years of experience, in data science, machine learning, machine learning engineering, or a related discipline
- Strong understanding of statistical modeling, experimentation, and evaluation frameworks
- Deep expertise in one or more ML domains such as NLP, deep learning, time-series, or clustering
- Hands-on experience pretraining or finetuning large transformer models (1B+ parameters) and building services that solve problems using LLMs and generative AI techniques
- Proven track record of implementing, deploying, and operating ML models in production environments at scale with high reliability
- Advanced programming skills in Python, with expertise in ML frameworks such as PyTorch and Hugging Face Transformers, and deep familiarity with pandas, scikit-learn, PySpark, and SQL/BigQuery
- Hands-on experience with cloud-based ML platforms and GPU infrastructure (e.g., Google Vertex AI, AWS SageMaker, Azure ML), container orchestration (Kubernetes, Docker), and orchestration tools (e.g., Kubeflow, Airflow, Prefect)
- Proficiency with ML experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, LangSmith, Langfuse), and with infrastructure-as-code (Terraform, Pulumi, Bicep) and GitHub Actions
- Strong understanding of model optimization techniques including quantization, distillation, pruning, and efficient inference strategies
- Proficient with coding agents (e.g., Claude Code, Codex) and agentic engineering best practices
- Ability to operate independently, think strategically, ensure execution, and influence others
- Proven ability to effectively communicate with all levels of the organization
- Commitment to continuous learning and staying current with the rapidly evolving ML and foundation model landscape
Responsibilities
- Apply rigorous statistical and machine learning techniques to analyze billions of workforce management and HCM records and identify meaningful product and feature opportunities
- Develop and deploy modern Data Science/AI solutions including generative AI applications, RAG systems, and agentic workflows
- Lead pretraining and finetuning efforts for large transformer models on UKG’s proprietary datasets, applying techniques such as LoRA, QLoRA, RLHF, and DPO
- Establish evaluation frameworks to measure model quality, business impact, and regression risk across model updates
- Lead the research, design, and implementation of end-to-end ML/AI pipelines in development and production environments
- Own the full model lifecycle: validation, deployment, monitoring, and iteration–including data drift detection, performance degradation alerts, and automated retraining triggers
- Build and operate model serving infrastructure that delivers low latency, high availability, and cost efficiency at scale
- Optimize training and inference costs through techniques such as mixed-precision training, quantization, distillation, and efficient batching strategies
- Write high-quality, modular production code and design clean data/model pipelines
- Implement CI/CD pipelines for ML workflows, including automated testing, validation, and canary deployment strategies for model updates
- Partner with Product Managers and domain experts to translate business requirements into scalable ML solutions
- Communicate model outcomes, infrastructure decisions, and system reliability to technical and non-technical stakeholders, including internal teams and external customers and partners
- Serve as the technical lead on data science and ML engineering initiatives, owning end-to-end decisions from problem framing and modeling through production operations
- Drive platform-wide standards for ML engineering, evaluation, and deployment
- Mentor data scientists through code reviews, model reviews, and modeling best practices, and proactively identify opportunities to improve team capabilities and development standards
Benefits
- flexibility that’s real
- benefits you can count on
- performance-based bonus plan
- restricted stock unit awards
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What This Job Offers
Job Type
Full-time
Career Level
Principal
