About this opportunity
Bonfirevc lists this Senior Product Data Scientist opportunity in broomfield, Colorado. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
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Worksite address
broomfield, CO, 80020, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.