Trustly is hiring a Manager for Data Science and Machine Learning (Risk and Fraud) in New York to oversee transaction decisioning and model deployment. This hands-on leadership role is well-suited for an experienced data science professional with a background in payments risk, fraud detection, and team management.
Role Overview
Trustly seeks a Manager of Data Science and Machine Learning to lead the team responsible for building and deploying transaction decisioning models in New York. This position requires a hands-on technical leader who will build production-ready models while guiding a team of machine learning engineers and data scientists. The role involves partnering across departments to translate complex risk challenges into practical machine learning solutions.
Key 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, including champion-challenger tests, holdouts, and staged rollouts.
Mentor and develop a team of full-stack data scientists and machine learning engineers.
Own the model roadmap across ACH return risk, fraud, and account takeover, ensuring focus on high-impact opportunities.
Maintain comprehensive model documentation, validation evidence, decision records, and audit trails.
Partner with Product and Engineering on scoring latency, feature availability, and integration with the risk engine.
Requirements
At least five years of experience building and deploying machine learning models in production, including two or more years leading a team.
Hands-on technical background with the ability to build, validate, and deploy a production model.
Strong Python skills and advanced SQL experience working with large datasets using distributed processing engines or cloud data warehouses.
Direct experience in fraud, credit risk, or payments risk, including rare-event modeling, delayed labels, and reject inference.
Experience with real-time inference and production model monitoring in a low-latency decisioning environment.
Experience owning or 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.
Nice to Have
A bachelor's degree in a quantitative discipline such as statistics, mathematics, computer science, economics, engineering, or operations research.
Proficiency with pandas and machine learning libraries such as scikit-learn, XGBoost, LightGBM, or CatBoost.
Knowledge of ACH or EFT payment systems, including NACHA return codes, return behavior, and return economics.
Experience working with open-banking or bank-transaction data.
Experience with a cloud machine learning platform such as Databricks, AWS SageMaker, or Google Cloud Vertex AI.
Experience with feature stores, streaming feature computation, real-time data pipelines, or rules and decision engines.
Skills and Tools
Python
SQL
Athena
Trino
Spark
Redshift
Snowflake
BigQuery
pandas
scikit-learn
XGBoost
LightGBM
CatBoost
Databricks
AWS SageMaker
Google Cloud Vertex AI
Salary and Benefits
Salary range of $195K – $280K
Offers Equity
Offers Bonus
About the Company
Trustly is building an open banking payment network to establish Pay by Bank as the standard at checkout. The company operates globally as a team of innovators, collaborators, and doers focused on creating a smarter, faster, and more secure financial future.
How to Apply
Interested candidates should apply on the official company careers page.
