Sr Director of Data Science leading payments and risk data science at DailyPay. Overseeing team of data scientists and advanced model deployment in a regulated environment.
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.
Talent and Team Development: 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.
Stakeholder Alignment: 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.
Payment Models: Directly oversee the development and deployment of Earned Wage Access payment models designed to safely advance pay to workers while maintaining loss guardrails.
Fraud Prevention: 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.
Loss Mitigation: Drive the successful build-out and implementation of predictive loss models and rules to pre-empt operating losses from advances.
Loss Forecasting & Compliance: 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.
Automation and Efficiency: 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.
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.
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