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Senior Data Science Product Manager Company Overview

Straddle is building the intelligence layer for modern payments, enabling smarter, faster, and more reliable financial decisions through data and machine learning. We operate at the intersection of fintech, data infrastructure, and real-time decisioning, where the models and insights we build directly impact transaction success, fraud prevention, and customer experience.

We are a fast-moving, high-ownership team that values speed, clarity, and pragmatic execution. We believe in delivering impact quickly, iterating continuously, and building systems that scale as the business grows.

Position Overview

We are seeking a Senior Data Science Product Manager to drive the discovery, scoping, and cross-functional orchestration of Straddle's data and ML-powered product capabilities.

This role bridges the gap between data science, product, and the market. You will work closely with product leadership to understand Straddle's product roadmap, identify where data and ML can create differentiated value, and translate those opportunities into well-scoped, high-impact data product initiatives. Examples include intelligent routing systems that maximize bank connection success across providers, balance prediction models that reduce payment failures and unlock new product offerings like guaranteed payments, and risk scoring features that shape how payment products are priced and rolled out.

Today, data product strategy and roadmap ownership sits with the Head of Data Science. As the team scales, this role will serve as the connective tissue between the data science team and the rest of the organization, engaging directly with customers, attending industry events, understanding the payments landscape, and channeling market needs back into the data product roadmap. You will drive discovery, scoping, and cross-functional coordination for data initiatives, and be a strong voice contributing to leadership's Data Roadmap and OKRs.

The ideal candidate is someone who thinks like a product manager but speaks the language of data science. Comfortable scoping an ML feature, challenging a model's assumptions, and presenting a data product strategy to leadership in the same week.

Essential Functions

• Drive discovery, scoping, and cross-functional coordination for data science and ML initiatives that support Straddle's core payment products. Surface opportunities, write proposals, and keep projects on track in partnership with the Head of Data Science

• Partner with product leadership to understand the full product landscape and identify where data-driven capabilities (models, features, scoring, intelligence) can create competitive advantage

• Translate product and business needs into well-defined data science project briefs, including problem framing, success metrics, data requirements, and delivery milestones

• Engage directly with customers, prospects, and partners to understand real-world payment challenges and surface opportunities for data products

• Represent Straddle's data capabilities externally at industry events, fintech meetups, and partner conversations. Bring market intelligence back to the team

• Collaborate with data science and engineering to ensure data products are built with the right trade-offs between speed, accuracy, and scalability

• Identify data gaps where acquiring new data sources, improving data quality, or connecting to new providers can meaningfully improve product and model outcomes

• Define and track success metrics for data products post-launch, driving iteration based on real-world performance

• Manage intake and triage of cross-functional data requests, providing recommendations on prioritization to the Head of Data Science

• Build and maintain PRDs and product proposals for data science initiatives, ensuring alignment across product, engineering, and leadership

Desired Experience & Skills

• 5+ years in product management, data science, or a hybrid data product role

• Strong understanding of machine learning concepts. You don't need to build models, but you need to know what's feasible, what's hard, and what questions to ask

• Demonstrated experience translating business problems into data/ML product requirements

• Track record of shipping data-powered features or products in a B2B or fintech context

• Strong product intuition. You understand user needs, market dynamics, and how to prioritize ruthlessly

• Experience working directly with customers or in customer-facing contexts (sales engineering, solutions, product discovery)

• Familiarity with payments, open banking, risk/fraud, or financial services is strongly preferred

• Excellent communication skills. You can write a clear PRD, run a stakeholder review, and present to leadership with equal comfort

• Comfort operating in ambiguity. You thrive when the problem isn't fully defined yet

• Experience with data platforms (Databricks, SQL, analytics tools) is a plus

Technical Familiarity

• Machine learning product lifecycle: problem framing, feature design, model evaluation, deployment, monitoring

• Data infrastructure concepts: pipelines, feature stores, lakehouse architecture, data quality

• Payment systems: ACH, RTP, open banking, identity verification, risk scoring

• A/B testing and experimentation design

• Analytics and BI tools (dashboards, cohort analysis, funnel metrics)

• Familiarity with Linear, Notion, or similar product management tooling

Culture Fit

• Speed over perfection — momentum creates opportunity; we deliver, iterate, and improve

• Ownership mentality — we don't stop at "our part"; we ensure outcomes

• Honest, data-driven thinking — we trust the data, even when it's inconvenient

• Curiosity and creativity — we ask "why," explore ideas, and challenge assumptions

• Pragmatic execution — we balance long-term scalability with immediate business impact

• Collaborative mindset — we think out loud, share context, and make each other better

We are building systems that directly impact real financial outcomes. That responsibility demands high standards, strong judgment, and a bias toward action.