Staff Data Scientist, Transfers

In this role, you’ll have the opportunity to:

• Own the end-to-end analytical and modeling strategy for Account Transfers, operating with high autonomy in a complex, ambiguous problem space with material client and business impact.
• Execute deep investigations across large, complex structured and unstructured datasets to uncover systemic operational failures, root causes, and improvement opportunities within the transfers ecosystem.
• Design and build scalable data models and analytical frameworks that enable proactive identification of transfer failures, early risk detection for at-risk client transfers and actionable signals for CXO and Product teams.
• Apply advanced modeling techniques (e.g., clustering, segmentation, predictive modeling, causal analysis) to identify distinct transfer behaviour archetypes, surface institutional-level patterns across external counterparties, and inform differentiated operational and product strategies.
• Partner closely with Product, CX, Operations, and Engineering leaders to translate analytical insights into product roadmap decisions, operational process design,  and client-facing interventions that measurably improve outcomes.
• Build and productionize data products (ML models, dashboards, decision tools) that embed analytics directly into workflows and decision-making.
• Measure and communicate impact, tying analytical work to improvements in transfer success rates, client experience, operational efficiency, and retention.
• Act as a technical and analytical leader by:
• Defining best practices for modeling, experimentation, and measurement in the transfers domain.
• Mentoring senior data scientists and raising the bar for analytical rigor.
• Influencing data strategy beyond immediate team boundaries.

What you bring:

• 7–10+ years of experience in data science, applied statistics, analytics, machine learning, or operations research and at least 3-5 years in Senior or Staff-level roles.
• Strong background working with complex, multi-step operational systems (e.g., operations, payments, transfers, risk, fraud, compliance, customer lifecycle, or similar domains).
• Experience applying operations research techniques such as optimization, simulation, queuing theory, or decision modeling to improve operational outcomes.
• Deep expertise in statistical analysis, experimentation, and inference on real-world dataHands-on experience building predictive and prescriptive models (e.g., classification, regression, anomaly detection, routing).
• Expert SQL skills, including complex joins, window functions, and performance optimization
• Excellent Python skills for data analysis, modeling, optimization, and production-ready workflows.
• Experience working with large, messy, and incomplete datasets, including both structured and unstructured data.
• Excellent communication skills, with experience presenting complex analytical or optimization results to non-technical stakeholders and influencing product strategy, operational decisions or roadmap priorities.
• Track record of driving measurable business or client impact through data science and/or operations research work.
• Experience mentoring or coaching senior data scientists and raising analytical standards within a team.
• Comfortable operating with high autonomy and making judgment calls under uncertainty