Senior Staff Data Scientist

DailyPay•New York, NY

2d•$215,000 - $294,000•Onsite

About The Position

DailyPay is seeking a Senior Staff Data Scientist to serve as the technical architect and strategic leader for our Personalization & AI Platform domains. This is one of the most technically senior and strategically influential roles in our data organization at the intersection of user experience, financial modeling, and AI infrastructure. You will architect the systems that make every DailyPay product interaction smarter - from how we optimize financial decisions for workers, to how we personalize communications, to how we protect members from fraud. Equally important, you will lay the data, infrastructure, and process foundations that enable your team and the broader data science function to operate with speed, rigor, and scale. This role requires someone who can move fluidly between writing production-grade model code, architecting scalable systems, partnering strategically with Product and Engineering leadership, and raising the technical bar of everyone around them.

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
Expert-level proficiency across modern AI, classical ML models, probabilistic methods, optimization techniques and causal inference; strong track record of translating business objectives into model strategy
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 identify where DS/AI creates competitive advantage, prioritize the DS/AI roadmap across product areas, and communicate model performance in terms of business outcomes to senior leadership
Experience coaching and mentoring senior ICs; ability to shape team technical direction and help build a culture of rigor and inclusion
Actively follows emerging AI research, tools, and frameworks; contributes to the long-term AI/ML capability roadmap; balances stakeholder work, new development, and technical debt
You are not satisfied when only your own work is excellent; you invest in the tools, standards, and people that make the team around you better and measure your impact accordingly
You form strong opinions from evidence and defend them clearly — to an engineer debating architecture, a product manager weighing tradeoffs, or an executive asking what to bet on
You have seen what happens when a great model meets a poorly designed production system, and build with that lesson baked in, thinking about monitoring, failure modes, and retraining before the first line of model code is written

Nice To Haves

Experience in fintech, payments, consumer financial products, or similar regulated domains strongly preferred

Responsibilities

Architect and own end-to-end ML systems for personalizing the user experience, including on-demand pay balance optimization
Design and deploy content personalization systems including 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
Establish the data infrastructure foundation the data science team requires: feature stores, training pipelines, labeling systems, data contracts, and monitoring frameworks
Define and enforce data quality standards; design automated validation and alerting pipelines that protect downstream model and business health, implement CI/CD best practices for ML
Partner directly with product and engineering leads to identify and quantify high-value AI opportunities and translate ambiguous business problems into multi-quarter technical roadmaps
Communicate complex technical outcomes to executive stakeholders as concrete strategic recommendations, grounded in business impact and financial metrics
Serve as the primary technical leader for a growing data science team, setting the technical direction, establishing standards, and mentoring data scientists at all levels through design reviews, architectural guidance, and code reviews
Cultivate a culture of rigor, curiosity, and operational excellence; establish documented, standardized patterns that make the entire team more effective
Drive cross-functional alignment on DS/AI success metrics, ensuring model performance is durably connected to measurable business outcomes
Establish team norms and a shared framework for effectively and responsibly applying modern AI; setting expectations around when and how to use AI tools, how to evaluate their outputs, and how to maintain rigor as capabilities evolve rapidly