Back to Jobs
Senior Data/ML Engineer
$150k – $190k base
1
Denver, CO
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 systems we build directly impact transaction success, fraud detection, 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/Staff ML/Data Platform Engineer to own the design and implementation of our data and machine learning platform.
This role spans data engineering, ML engineering, and MLOps, with responsibility for building a scalable lakehouse architecture, productionizing models, and enabling real-time and batch decisioning systems.
This is a hands-on role requiring strong individual contribution across system design, coding, and deployment. The ideal candidate can balance speed and scalability, make pragmatic trade-offs, and operate with high ownership in a fast-paced startup environment.
Essential Functions
Design and build scalable data pipelines for ingesting and processing transactional and event data
Architect and implement a Databricks-based lakehouse using Delta Lake and Unity Catalog
Establish data governance standards (access control, lineage, data quality, compliance)
Build and maintain feature pipelines and feature store infrastructure
Deploy machine learning models in batch and real-time environments
Implement CI/CD pipelines for data and ML workflows within Databricks
Set up model monitoring, drift detection, and automated retraining pipelines
Design real-time and batch processing architectures based on business needs
Develop dashboards and analytics to monitor product, model, and business performance
Manage and optimize data infrastructure, storage, and database systems
Translate business problems into scalable data and ML solutions
Collaborate cross-functionally with data science, engineering, and product teams
Continuously improve system performance, scalability, and reliability
Desired Experience & Skills
5+ years in data engineering, ML engineering, or related roles
Strong experience building production-grade data pipelines (ETL/ELT)
Proficiency in R/Python and SQL
Experience with Databricks and Apache Spark
Experience with cloud platforms (preferably Azure)
Experience deploying ML models into production systems
Familiarity with CI/CD, containerization (Docker), and DevOps practices
Experience with ML lifecycle tools (e.g., MLflow, Kubeflow, Vertex AI)
Strong problem-solving and debugging skills
Ability to work across ambiguous, evolving requirements
Strong communication and collaboration skills
Technical Expertise
Databricks ecosystem (Delta Lake, Unity Catalog, MLflow)
Data modeling, warehousing, and lakehouse architectures
Feature engineering and feature store design
Batch and real-time data processing (e.g., Spark, Kafka, streaming systems)
REST APIs / microservices for model serving
Data quality, observability, and monitoring frameworks
Performance optimization for large-scale data systems
Security and compliance for sensitive financial data
Culture Fit
At Straddle, data science and engineering are guided by a shared philosophy:
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.
Apply now
Your application will be reviewed by us. We'll get back to you quickly. We can't wait to meet you!
About the job
Job type
Full-Time, Hybrid
Salary
$150k – $190k base
Benefit you’ll get
Flexible Work Environment – We offer hybrid and remote options so you can work where you’re most productive, whether that’s at home, in-office, or a mix of both.
Equity Ownership – As an early team member, you’ll receive equity in the form of options or RSUs—your contributions grow the company, and you share in the upside.
Unlimited PTO – Take the time you need to rest, recharge, or handle life outside of work. We trust our team to balance time off with results.
Health & Wellness Coverage – Comprehensive medical, dental, and vision plans help keep you and your family healthy, with 100% employee premium coverage on select plans.
Paid Parental Leave – We support growing families with fully paid time off for new parents, including adoption and foster care.
Professional Development – We invest in your growth with paid courses, certifications, and conference opportunities tailored to your role and interests.
Home Office & Equipment Stipend – Receive a stipend to set up your home workspace and get the tools you need to work comfortably and effectively.
Team Retreats – We host regular offsites to align on strategy, collaborate face-to-face, and have fun as a team.
Autonomy & Ownership – We give you space to lead initiatives, own outcomes, and shape the direction of your work without micromanagement.
Mission-Driven Work – Help build infrastructure that moves money more efficiently, securely, and transparently for modern businesses.
Related position
Agentic Engineer
Build at the frontier of how software gets made. You'll direct fleets of AI coding agents to ship secure, scalable payments infrastructure, and own every outcome.
$160k – $200k base
6
Denver, CO
Find Out More
Senior Product Data Scientist
We are seeking a Data Science Product Manager to report into the data science function, driving the strategy and execution behind models that detect fraud, optimize payment routing, and surface actionable insights for our clients. You will work closely with data and engineering teams to ensure data products move from discovery to production with speed and clarity.
$150k – $190k base
1
Broomfield, CO
Find Out More
Data Science Product Manager
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.
Find Out More
Related position
Agentic Engineer
Build at the frontier of how software gets made. You'll direct fleets of AI coding agents to ship secure, scalable payments infrastructure, and own every outcome.
$160k – $200k base
6
Denver, CO
Senior Product Data Scientist
We are seeking a Data Science Product Manager to report into the data science function, driving the strategy and execution behind models that detect fraud, optimize payment routing, and surface actionable insights for our clients. You will work closely with data and engineering teams to ensure data products move from discovery to production with speed and clarity.
$150k – $190k base
1
Broomfield, CO
Data Science Product Manager
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.