Senior Data Scientist

About Us:
DailyPay is transforming the way people get paid. As a worktech company and the industry’s leading on demand pay solution, DailyPay uses an award-winning technology platform to help America’s top employers build stronger relationships with their employees. This voluntary employee benefit enables workers everywhere to feel more motivated to work harder and stay longer on the job while supporting their financial well-being outside of the workplace.
DailyPay is headquartered in New York City, with operations throughout the United States as well as in Belfast. For more information, visit DailyPay's Press Center.

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

This is a high-impact individual contributor role for a data 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.

• 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.

• Partner with product and engineering on scoped problem areas, translating defined business questions into well-structured DS solutions. Bring senior DS leadership in early on ambiguous or high-stakes problem framing.

• Communicate model results and tradeoffs clearly to product and cross-functional partners, connecting technical outputs to business outcomes.

• 5+ years of Data Science & Machine Learning experience within fintech, payments, or a similarly regulated consumer domain. With a proven track record of shipping production models that directly impact the end-user experience.

• Bachelor’s or advanced degree in a quantitative discipline (e.g., computer science, machine learning, statistics, data science).

• Track record of independently building models that drive measurable business outcomes; experience seeing your own work through to production, including partnering with stakeholder teams to define success metrics and connect model performance to business value.

• Experience building and maintaining reliable feature engineering pipelines, with advanced SQL and Python skills and a working knowledge of the data infrastructure that supports model development at scale.

• Hands on experience with end-to-end model deployment; data pipelines, model monitoring, drift detection, and A/B test execution, with a strong instinct for production reliability.

• Experience owning models in production environments where failures have real financial or compliance consequences.

• Strong proficiency across modern AI, classical ML, statistical, and probabilistic methods; experience translating well-defined business objectives into modeling approaches and evaluating them rigorously.