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Straddle is building the intelligence layer for modern payments, enabling smarter, faster, and more reliable financial decisions through data and machine learning. The company operates at the intersection of fintech, data infrastructure, and real-time decisioning, where models and insights directly impact transaction success, fraud prevention, and customer experience.
As Senior Data Science Product Manager, you will drive discovery, scoping, and cross-functional orchestration of Straddle's data and ML-powered product capabilities. This role bridges data science, product, and market needs. You will work closely with product leadership to understand the product roadmap, identify where data and ML create differentiated value, and translate opportunities into well-scoped, high-impact data product initiatives. Examples include intelligent routing systems that maximize bank connection success, balance prediction models that reduce payment failures and unlock new offerings like guaranteed payments, and risk scoring features that shape product pricing and rollout.
Key responsibilities include: driving discovery and scoping for data science and ML initiatives supporting core payment products; partnering with product leadership to identify where data-driven capabilities create competitive advantage; translating product and business needs into well-defined data science project briefs with problem framing, success metrics, and milestones; engaging directly with customers, prospects, and partners to understand payment challenges and surface data product opportunities; representing Straddle's data capabilities externally at industry events and partner conversations; collaborating with data science and engineering on product trade-offs between speed, accuracy, and scalability; identifying data gaps and new data sources to improve products and models; defining and tracking success metrics post-launch; managing cross-functional data request intake and prioritization; and building PRDs and proposals ensuring alignment across product, engineering, and leadership.
You should have 5+ years in product management, data science, or hybrid data product roles. Strong understanding of ML concepts is essential—you don't need to build models but must know what's feasible and what questions to ask. Demonstrated experience translating business problems into data/ML requirements, shipping data-powered features in B2B or fintech contexts, and working directly with customers is required. Familiarity with payments, open banking, risk/fraud, or financial services is strongly preferred. Excellent communication skills, comfort operating in ambiguity, and experience with data platforms (Databricks, SQL, analytics tools) are valued. Technical familiarity with ML product lifecycle, data infrastructure concepts, payment systems, A/B testing, and analytics/BI tools is important.
The culture emphasizes speed over perfection, ownership mentality, honest data-driven thinking, curiosity and creativity, pragmatic execution, and collaborative mindset.
