Staff Data Scientist - Payments

Lead and manage data science projects, ensuring timely delivery and alignment with business goals. Develop and maintain data models, algorithms, and reporting systems to support data analysis and decision-making. Analyze complex datasets to identify trends, patterns, and insights that drive strategic initiatives. Collaborate with cross-functional teams to understand data needs and provide actionable insights. Ensure data quality and integrity through regular audits and validation processes. Mentor and guide junior data scientists, fostering a culture of continuous learning and improvement. 5+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. Partner with Product, Engineering, and Finance to translate complex business problems into scalable data science solutions. Build causal inference and forecasting models to measure marketing and product impact, guiding investment and prioritization decisions. Design and operationalize experimentation frameworks to improve product performance and user engagement. Develop and maintain reusable data pipelines, embeddings, and feature libraries to accelerate model development. Communicate insights and recommendations clearly to leadership, influencing strategic direction across teams. Proven expertise in causal inference, experimentation, and model-driven decision-making. Proficiency in Python, SQL, and statistical modeling frameworks (e.g., XGBoost, regression, time series, DML). Experience in cloud environments (AWS, GCP) and analytics tools (Tableau, PySpark). Strong stakeholder management and ability to communicate insights to non-technical audiences. Experience leading experimentation and measurement strategies in large-scale tech environments. Experience in setting up Agentic workflows for automating data requests and root cause analysis Background in product analytics and marketing measurement. Demonstrated impact in cross-functional collaboration with product, engineering, and business stakeholders. Experience mentoring data scientists and driving adoption of data-driven decision-making practices.