IT Data Scientist Sr

Dayforce

2d

About The Position

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.

Requirements

• 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.
• Proficiency in English is required for this position as this role will regularly interact with English-speaking stakeholders, co-workers, managers and/or clients across the world. Further, our back office support teams, including but not limited to Human Resources, are primarily English speaking. Employees need to be able to communicate with these departments in English to appropriately administer their business relationship. Due to the significant high volume of interactions with these English-speaking co-workers, managers, stakeholders and/or clients, which is inherent to this position, it is not possible to reorganize the company's activities to avoid this requirement.

Responsibilities

• 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

Benefits

• Dayforce is fueled by the diversity of our talented employees. We are an equal opportunity employer and consider and embrace ALL individuals and what makes them unique. We believe our employees should be happy and healthy, with peace of mind and a sense of fulfillment.
• We encourage individuals to apply based on their passions.
• Dayforce encourages personal and professional growth. We offer excellent time away from work programs, comprehensive wellness initiatives and recognition through competitive pay and benefits.
• With a commitment to community impact, including volunteer days and our charity, Dayforce Cares we provide opportunities for you to thrive both in your career and personal life. Our focus is not just on your job but on supporting you to be the best version of yourself.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level