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About the Role

Lead the team responsible for building and deploying machine learning models for transaction decisioning, directly influencing risk management, fraud prevention, customer protection, and payment experience delivery. This hands-on leadership role involves developing production-ready models, setting technical direction, mentoring the team, and establishing strong modeling and experimentation practices.

Responsibilities

Build, validate, deploy, and continuously improve machine learning models for ACH return risk, fraud detection, and account takeover.
Develop real-time, online, and offline features using transaction, account, device, network, and behavioral data.
Design models that operate reliably in a low-latency, high-volume production environment.
Design and analyze experiments to measure the impact of model and policy changes.
Quantify the impact of model changes across loss rates, approval rates, customer experience, merchant performance, and overall return on investment.
Translate complex modeling results into clear insights and recommendations for nontechnical partners.
Mentor and develop a team of full-stack data scientists and machine learning engineers.
Hire, assess, and level talent based on technical depth, business judgment, and production system building ability.
Set a high bar for technical quality, execution, and accountability across the team.
Take ownership of team deliverables, including what ships, when it ships, and its production performance.
Own the model roadmap across ACH return risk, fraud, and account takeover.
Establish the team’s operating cadence, including prioritization, model reviews, launch readiness, performance reviews, and postmortems.
Create an environment that supports rapid movement while maintaining strong standards for testing, documentation, governance, and production reliability.
Own production model performance, including monitoring, drift detection, retraining schedules, model updates, and decisions about model modification or removal.
Maintain comprehensive model documentation, validation evidence, decision records, and audit trails.
Ensure production decisions are explainable with clear reason codes.
Partner with Product and Engineering on scoring latency, feature availability, system reliability, and integration with Trustly’s risk engine.
Co-own the decision strategy and policy layer with Risk Operations.
Evaluate and communicate tradeoffs between approval rates, fraud/loss rates, customer experience, and revenue.
Co-own loss forecasting with Finance and Risk.
Present model performance, loss drivers, emerging risks, and roadmap tradeoffs to senior and executive stakeholders.
Evaluate third-party risk data, tools, and model providers.

Requirements

At least five years of experience building and deploying machine learning models in production.
Two or more years leading a team.
Current ability to build, validate, and deploy a production model.
Strong Python skills.
Advanced SQL experience working with large datasets using a distributed processing engine or cloud data warehouse.
Direct experience in fraud, credit risk, or payments risk, including rare-event modeling, delayed labels, reject inference, and understanding the difference between strong AUC and effective business decisions.
Experience with real-time inference and production model monitoring in a low-latency decisioning environment.
Experience owning or materially contributing to a loss budget or loss forecast.
Strong experiment-design experience for model and policy changes.
Demonstrated experience mentoring and developing data scientists and machine learning engineers.
Strong communication and stakeholder-management skills.

Skills

Python
SQL
Fraud detection
Credit risk
Payments risk
Rare-event modeling
Delayed labels
Reject inference
Real-time inference
Production model monitoring
Low-latency decisioning
Drift detection
Retraining strategies
Model updates
Experiment design
Champion-challenger testing
Holdouts
Staged rollouts
Pandas
Scikit-learn
XGBoost
LightGBM
CatBoost
ACH payment systems
NACHA return codes
Return behavior
Return economics
Open-banking data
Bank transaction data
Databricks
AWS SageMaker
Google Cloud Vertex AI
Feature stores
Streaming feature computation
Real-time data pipelines
Rules and decisioning platforms
Sequence models
Graph-based models
Recurrent architectures
Entity-link analysis
Model-risk management
Governance requirements
Third-party risk data
Fraud tools
External model providers

Location

San Francisco
New York

Work Type

Hybrid

Experience Level

Manager
5+ years of experience
2+ years of team leadership

Education Level

Bachelor’s degree in a quantitative discipline (statistics, mathematics, computer science, economics, engineering, or operations research) preferred

Benefits

Flexible paid time off
Generous PTO accrual plans
Comprehensive medical, dental, vision, and other insurances
FSA & HSA plans for medical and dependent care
Home office set-up allowance
Internet stipend
Retirement plan match for 401k and RRSP
Gender-neutral paid parental leave

About the Company

Trustly is a global leader in Open Banking Payments, revolutionizing the world of payments by building a smarter, faster, and more secure financial future.
Our ambition is to build the world’s most disruptive payment network and redefine the payment experience.
We are a global team of innovators, collaborators, and doers operating in a dynamic, entrepreneurial, and high-growth environment.
The Data team is the backbone of Trustly’s decision-making and product innovation, processing millions of transactions in real time and turning complex Open Banking signals into seamless payment experiences.
We foster a culture of intellectual curiosity, technical excellence, and extreme ownership.

Equal Opportunity

Trustly embraces and celebrates diversity of all forms.
We are a proud and committed Equal Opportunity Employer.
We believe an open and inclusive environment enables people to do their best work.
All decisions regarding hiring, advancement, and any other aspects of employment are made solely on the basis of qualifications, merit, and business need.