Job Description

About the Opportunity
We are seeking a Senior Data Scientist who is a systems thinker, practical builder, and strong communicator to join a small, high-impact data science team within our broader data analytics organization at Dayforce. This role focuses on applying machine learning, advanced analytics, and emerging Agentic AI techniques to real business problems--driving insights, automation, and decision intelligence from concept through production.
What you'll get to do
Lead end-to-end data science initiatives from problem definition and requirements gathering through model development, deployment, and insight delivery
Design and build machine learning solutions that are practical, scalable, and aligned to business objectives
Develop and implement agentic AI workflows that combine ML models, large language models (LLMs), data retrieval, and tool-based execution
Partner closely with business stakeholders to translate ambiguous questions into analytical and AI-driven solutions
Model training, containerization, deployment, and performance monitoring
Evaluate tradeoffs between model accuracy, interpretability, cost, and operational complexity
Automation, observability, and lifecycle management of ML pipelines (MLDC / MLOps)
Familiarity with cloud data and compute platforms (e.g., Databricks, Snowflake, AWS, Azure)
Communicate insights, recommendations, and limitations clearly to technical and non-technical audiences
Promote strong data quality, validation, and governance practices
Mentor and collaborate with other data scientists and analytics professionals
Skills and experience we value
Bachelor's degree in Computer Science, Data Science, Engineering, Applied Mathematics, Statistics, or equivalent experience (Master's degree preferred).
At least six years of experience working with data and four or more years of hands-on data science or machine learning experience.
Solid foundation in machine learning techniques, including regression, classification, clustering, and time series forecasting.
Experience in feature engineering, model evaluation, bias detection, and interpretability.
Practical experience integrating LLMs with structured data and machine learning models.
Familiarity with AI orchestration frameworks, vector databases, and RAG patterns.
Experience with ETL processes, data modeling, and working across multiple data sources.
Proficiency with Power BI or similar data visualization tools.
Exposure to data governance, security, and model monitoring best practices.

Skills Required