AI & ML Scientist

About Us:
DailyPay is the leader in On-Demand Pay, helping employers modernize how people get their pay. DailyPay serves more than 1,900 employers and over 6 million employees, including many of the world's most recognized brands. By providing real-time access to earned pay and financial wellness tools, DailyPay helps employees manage their finances and helps employers attract and retain talent. DailyPay is helping define the future of pay, where money moves at the speed of work. Learn more at DailyPay's Press Center.

The Role:

DailyPay is seeking a Senior AI & ML Scientist to join our Data Science team.

This is a high-impact individual contributor role for an AI & ML scientist who executes complex modeling work with excellence and is growing their strategic influence across product and business stakeholders.

You will build and maintain the models and decision systems that personalize the DailyPay product experience — from optimizing financial decisions for workers, to personalizing communications and user experiences, to supporting fraud controls. You will contribute to the data infrastructure the team needs and follow established standards for production-grade ML.

This role is right for someone who does excellent, rigorous hands-on work and is ready to grow their ability to translate that work into clear business impact.

If this opportunity excites you, we encourage you to apply even if you do not meet all of the qualifications.

How You Will Make an Impact:

• Build, improve, and maintain ML models for personalizing the user experience including on-demand pay balance optimization, content personalization, and fraud controls.

• Build reliable data pipelines and features for model development, following team infrastructure standards. Identify gaps in data infrastructure along the way and advocate for solutions with engineering partners.

• Develop, evaluate, deploy, monitor, and improve both batch and real time models following established production standards.

• Stay current on AI/ML developments and apply sound judgment in algorithm selection and technique adoption by evaluating tradeoffs across modeling approaches and recommending the best tool for the problem.

• Write clean, well-documented, traceable, versioned, and reproducible code across all model development and pipeline work, following team standards for maintainability and auditability.

• Follow and contribute to data quality standards and validation practices; flag issues proactively and help improve team patterns.