Enterprise AI Data Scientist — Finance Risk & Prediction

Dayforce; Omaha (NE); On-site; USD 120,000 - 180,000; Full time

Job summary

Dayforce in Omaha seeks an AI Data Scientist to own the modeling lifecycle of enterprise AI initiatives, partnering with business operations to structure financial data and build predictive models that deliver high-accuracy insights and reduce risk. The role requires a rigorous quantitative expert balancing statistics with regulatory standards and cross-functional alignment.

Key responsibilities include developing models for anomaly detection and forecasting, validating accuracy, and

Qualifications

• 5+ years of experience building and validating predictive models in a corporate or financial environment.
• Demonstrated ability to apply advanced statistics and machine learning to deliver measurable business outcomes.
• Deep expertise in statistical analysis, Python, R, and data science frameworks.
• Ability to present complex quantitative findings to non-technical stakeholders.
• Operator mindset with a focus on execution and analytical outcomes.

Responsibilities

• Develop and train advanced predictive models for anomaly detection, workflow forecasting, and risk mitigation
• Perform deep statistical analysis and data wrangling within large-scale enterprise environments
• Validate model accuracy rigorously, test for bias, and ensure all outputs meet strict financial compliance standards
• Partner with business leaders to understand the nuance of financial data and transform raw inputs into training datasets
• Establish KPIs tied to model accuracy, prediction precision, and false-positive reduction

Skills

Predictive modeling

Statistical analysis

Python

R

Data science frameworks

Stakeholder communication

Job description

Dayforce in Omaha seeks an AI Data Scientist to own the modeling lifecycle of enterprise AI initiatives, partnering with business operations to structure financial data and build predictive models that deliver high-accuracy insights and reduce risk. The role requires a rigorous quantitative expert balancing statistics with regulatory standards and cross-functional alignment.

Key responsibilities include developing models for anomaly detection and forecasting, validating accuracy, and