Position Overview

We are seeking a Senior Product Data Scientist to be a hands-on technical leader on the data science team, someone who builds models and ships features while also shaping what gets built and why.

This role combines deep data science execution with product-level thinking. You will own the full modeling lifecycle, from problem framing through deployment and monitoring, while also engaging with customers, product leaders, and the broader payments ecosystem to identify where data and ML can create the most impact. 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.

This is not a planning role. You are expected to build. But you also bring the product context that ensures you're building the right things. You understand customer pain points, can spot where a new data source or model could unlock a product opportunity, and you translate that into working code and production models. You will be a strong voice contributing to leadership's Data Roadmap and OKRs, and a key partner to the Head of Data Science in setting the technical and strategic direction of the team.

Essential Functions

• Own modeling strategy end-to-end: problem framing, feature selection, algorithm design, training, evaluation, and iteration

• Build and ship production models for risk scoring, fraud detection, payment decisioning, balance prediction, and customer segmentation

• Design and build features from transactional, behavioral, and open banking data, identifying where new data sources can meaningfully improve model performance

• Establish rigorous evaluation frameworks. Select appropriate metrics, build holdout/backtesting strategies, and measure real-world model performance

• Collaborate with ML/data engineering to deploy models into batch and real-time production systems

• Monitor model performance post-deployment, detect drift, and drive retraining or redesign when needed

• Engage directly with customers, prospects, and partners to understand real-world payment challenges and translate them into data science opportunities

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

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

• Write product proposals and project briefs for data science initiatives, including problem framing, success metrics, data requirements, and delivery milestones

• Identify data quality issues, coverage gaps, and opportunities to bring in external data that strengthens product and model outcomes

• Help set technical direction and raise the bar on rigor across the data science team

Desired Experience & Skills

• 6+ years in applied data science, machine learning, or quantitative analytics roles, with a track record of shipping models into production

• Strong foundation in statistics, probability, and machine learning, with a clear understanding of why you choose specific algorithms, not just how to use them

• Demonstrated product sense. You understand how models connect to business outcomes and can identify the highest-leverage problems to solve

• Proficiency in R or Python, and SQL

• Experience building and evaluating classification, regression, and ranking models in production contexts

• Experience with feature engineering from complex, messy, real-world data

• Familiarity with model deployment workflows and monitoring (e.g., MLflow, Databricks, CI/CD pipelines)

• Strong data intuition. Ability to spot issues in data quality, distribution shifts, and feature leakage

• Excellent communication skills. Can write a clear product brief, present to leadership, and explain model trade-offs to non-technical stakeholders

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

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

• Experience in fintech, payments, or fraud/risk is strongly preferred

• Prior experience in a senior or lead IC role, or experience managing a small team, is a plus

Technical Expertise

• Machine learning fundamentals: supervised/unsupervised learning, ensemble methods, model selection, hyperparameter tuning, cross-validation

• Statistical analysis, hypothesis testing, and experimental design (A/B testing)

• Feature engineering and feature store design for transactional and behavioral data

• Model evaluation: precision/recall trade-offs, calibration, lift analysis, backtesting

• Model monitoring, drift detection, and performance degradation analysis

• Databricks ecosystem (Delta Lake, MLflow, Spark) preferred

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

• Experience working with financial, identity, or payment data at scale

• Familiarity with AI agents, LLMs, or applied GenAI for product use cases is a plus

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.