Sr Director, Data Science

DailyPay•New York, NY

About The Position

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 looking for a Sr Director of payments and risk data science to lead a team of data scientists, strategists and analysts to define the strategy, vision, and roadmap for Payments & risk data science. Reporting to the Chief Product Officer, the ideal candidate is a hands-on leader and a visionary who can transition the team from traditional modeling to next-generation machine learning platforms, leveraging emerging data sources (e.g., cash flow, alternative bureaus) to significantly improve model performance, reduce losses, and prevent fraud. This role requires exceptional organizational leadership, an ability to influence executive stakeholders, and proven success in delivering complex models into a regulated production environment.

Requirements

15+ years of progressive experience in payments, risk and fraud with at least 7 years in a senior leadership/management role (managing managers and/or technical leads).
Proven track record of building and mentoring expert AI/ML teams that consistently deliver innovative solutions, resulting in measurable business impact and sustained competitive advantage
Deep familiarity with the payments ecosystem (issuing, acquiring, gateways, ACH/Rails) and the unique adversarial nature of financial fraud.within a regulated financial institution (FinTech, Bank, or similar)
Advanced Degree: Ph.D. in Computer Science, Statistics, Mathematics, Physics, Operations Research, or a related quantitative field is highly preferred.
Applied AI Scale: Experience deploying GNNs or Transformer-based models in a high-throughput, low latency production environment (not just offline research).
Transformation Experience: A track record of modernizing legacy model stacks (e.g., moving from logistic regression/forests to Deep Learning/AI) in a large enterprise.
Tools & Platforms: Expert-level proficiency in Python (PySpark, scikit-learn, TensorFlow/PyTorch) and SQL/data warehouse technologies (e.g., Snowflake, Hive). Familiarity with modern MLOps platforms and cloud computing (AWS).
Communication: Exceptional executive presence and the ability to distill highly complex analytical concepts into clear, concise, and compelling narratives for non-technical leadership.

Responsibilities

Define and Champion Strategy: Develop and articulate the 1-3 year roadmap for Data Science, aligning all priorities with the broader product/business objectives.
Drive Next-Generation Capabilities: Incorporate industry trends and advanced techniques (NLP, Graph Mining, LLMs, Deep Learning) to solve complex, high-impact risk problems where established principles may not fully apply.
Lead the current team of high-performing Staff and Senior Data Scientists. Recruit, mentor, and foster talent through deliberate interactions, succession planning, and creating a high-accountability, low-ego culture.
Interact and negotiate with senior management and Product Leads to reconcile competing views and drive critical, high-impact business decisions.
Influence the direction of the company's AI/ML strategy and contribute to long-term planning.
Directly oversee the development and deployment of Earned Wage Access payment models designed to safely advance pay to workers while maintaining loss guardrails.
Develop advanced fraud prevention measures and models embedded into real-time decisioning to protect from increasingly sophisticated threats—spanning identity theft, synthetic fraud, account takeovers, and scams—before they happen.
Drive the successful build-out and implementation of predictive loss models and rules to pre-empt operating losses from advances.
Lead the development of Loss Forecasting and CECL models, ensuring they align with industry practices and meet all regulatory requirements for the firm's balance sheet and reserve calculations.
Lead efforts to automate model monitoring and governance processes (MLOps) to create scalable and auditable infrastructure.
Collaborate with cross-functional teams to integrate ML models into products and services.