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We are looking for an experienced Data scientist to join Block’s AI, Data, and Analytics (AiDA) team. This role will support the Financial Platform (FinPlat) organization, focusing on “acquiring side” A|B testing, reporting, strategic analysis, and automation. FinPlat is the “central nervous system” of Block, enabling the movement and ledgering of funds for our three main businesses: Square, Cash App, and AfterPay. Additionally, FinPlat provides significant financial impact through optimization of transaction costs and authorization rates.
Job Responsibility:
Establish subject-matter expertise in payment rails, focusing on cards (pre-paid, debit, credit, etc.) and bank rails (wire, ACH, direct deposit, etc.)
Influence product roadmaps and guide the Financial Operations (FinOps) team to navigate through product opportunities and problem ambiguities
Build “soup to nuts” data products, taking raw eventing data from engineering teams and turning into actionable analyses and ML models
Partner with Engineering and Operations leadership to drive a culture of data-driven financial impact
Expand your influence in the Block Data Science community by developing and evangelizing data solutions to improve analytics insightfulness and actionability
Requirements:
8+ years of related experience (data science, preferably in payment analytics, chargebacks, or related fields) with Bachelor's degree
or 6 years and a Master’s degree
or a PhD with 3 years experience
or equivalent experience
At least a bachelor's degree in a technical field such as Engineering, Physics, mathematics, computer science, etc.
Experience working on ambiguous problems with high business impact and resolving complex issues in innovative and efficient ways
Expertise in hypothesis testing and causal inference
A strong understanding and willingness to be hands-on in building data pipelines and architecture
Ability to build strong relationships with cross-functional stakeholders, clearly communicating findings and influencing product designs
Advanced proficiency with data, analytics, and visualization technologies (we use SQL, Looker, and Python)