Senior Staff Data Scientist

DailyPay, Inc.

Job Overview

Location

NYC Headquarters

Job Type

Full-time

Full Job Description

📋 Description

• Architect and own end-to-end machine learning systems for personalizing the DailyPay user experience, including on-demand pay balance optimization, email content ranking, in-app experience sequencing, and offer relevance
• Lead the transition from offline batch scoring to real-time, low-latency inference pipelines embedded directly in product backends to enhance user interactions with minimal delay
• Design and establish the foundational data infrastructure for the data science team, including feature stores, training pipelines, labeling systems, data contracts, and monitoring frameworks
• Define and enforce data quality standards through automated validation and alerting pipelines to protect downstream model performance and business health
• Implement CI/CD best practices specifically tailored for machine learning systems to ensure reliable, repeatable deployment and rollback capabilities
• Partner directly with Product and Engineering leadership to identify high-value AI opportunities, translate ambiguous business problems into multi-quarter technical roadmaps, and quantify impact using financial metrics
• Communicate complex technical outcomes to executive stakeholders as strategic, business-grounded recommendations tied to measurable outcomes such as engagement, retention, and fraud reduction
• Serve as the primary technical leader for a growing data science team, setting technical direction, conducting design reviews, providing architectural guidance, and performing code reviews for junior and senior data scientists
• Cultivate a culture of rigor, curiosity, and operational excellence by documenting standardized patterns, establishing team norms, and institutionalizing best practices for AI application and evaluation
• Drive cross-functional alignment on DS/AI success metrics to ensure model performance is durably connected to concrete business outcomes, not just technical benchmarks
• Establish a responsible AI framework that defines when and how to use AI tools, how to evaluate their outputs, and how to maintain methodological rigor as technologies evolve rapidly
• Maintain expert-level proficiency in modern AI, classical ML models, probabilistic methods, optimization techniques, and causal inference to translate business objectives into robust model strategies
• Deploy production-grade ML systems to engineering standards with deep understanding of data pipeline development, model observability, drift detection, latency/cost tradeoffs, incident response, and recovery planning
• Actively follow emerging AI research, tools, and frameworks to shape the long-term AI/ML capability roadmap while balancing stakeholder deliverables, new development, and technical debt
• Operate as a force multiplier by investing in team tools, standards, and mentorship—measuring personal impact through the collective excellence of the data science organization
• Apply an operator’s instinct to production systems, anticipating failure modes, monitoring needs, and retraining requirements before writing the first line of model code
• Defend strong, evidence-based technical opinions clearly to engineers, product managers, and executives, ensuring architecture decisions are aligned with business priorities and scalability constraints

🎯 Requirements

• Advanced degree in a quantitative discipline (e.g., computer science, machine learning, data science, engineering) with 10+ years of industry experience in data science and machine learning
• Demonstrated history of architecting and deploying production ML systems that drive significant, measurable business ROI, preferably across multiple product domains
• Experience in fintech, payments, consumer financial products, or similar regulated domains strongly preferred
• Expert-level proficiency in modern AI, classical ML models, probabilistic methods, optimization techniques, and causal inference
• Deep expertise in end-to-end production deployment to engineering standards: data pipeline development, model observability, monitoring, drift detection, latency/cost tradeoffs, incident response, and rollback planning
• Proven ability to mentor senior individual contributors, shape team technical direction, and build a culture of rigor and inclusion

🏖️ Benefits

• Exceptional health, vision, and dental care
• Opportunity for equity ownership
• Life and AD&D, short- and long-term disability
• Unlimited PTO
• 401K with company match
• Employee Assistance Program

Skills & Technologies

Data Science

Senior

Onsite

Degree Required

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DailyPay, Inc.

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About DailyPay, Inc.

DailyPay provides an on-demand pay platform that integrates with employer payroll systems, allowing employees to access earned wages before the scheduled payday. Founded in 2015 and headquartered in New York City, the company partners with enterprises across retail, hospitality, healthcare and contact-center industries to offer real-time pay transfers, automated savings, financial counseling and analytics dashboards that reduce turnover and support workforce financial wellness.

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