Manager, Data Science - US Remote

Manager, Data Science - US Remote )

This role is 100% remote and can be located anywhere in the US.

Did you know there are 1.4 billion people in the world that are financially underserved by traditional banks? In many cases, people depend on remittances being sent or received across different countries or continents. Money Gram impacts the daily lives of 1.5 million customers, connecting families and businesses across borders. By relying on a vast network of agents and developing cutting‑edge payment technology, Money Gram is paving the way for global financial fairness and inclusion.

The role is responsible for designing, developing, and deploying production fraud detection models that score transactions in real time. It co‑owns the end‑to‑end data science roadmap for transaction fraud and risk, builds and maintains feature pipelines leveraging transaction data, device signals, behavioral patterns, and identity attributes, and leads the transition from rules‑based fraud detection to a model‑first decisioning architecture.

• Design, develop, and deploy production fraud detection models that score transactions in real time.
• Co‑own the end‑to‑end data science roadmap for transaction fraud and risk.
• Build and maintain feature pipelines using transaction data, device signals, behavioral patterns, and identity attributes.
• Lead the transition from rules‑based fraud detection to model‑first decisioning architecture.
• Design the interaction between models and rules, determining when models make primary decisions versus rules.
• Implement champion/challenger frameworks to continuously test and improve both model and rule performance.
• Create monitoring systems for model drift, feature distribution shifts, rule effectiveness, and overall system performance.
• Generate reason codes and explainability outputs for every model decision.
• Mentor and lead a small team of data scientists while remaining hands‑on with development.
• Partner with Risk Intelligence team to align model and rule strategies with business objectives.
• Present performance analysis, trade‑off recommendations, and strategic roadmaps to leadership.

• 7+ years of progressive experience in machine learning, data science, or quantitative risk.
• 4+ years building production ML models in fraud, risk, payments, or financial services.
• 3+ years working with rule‑based fraud detection systems, including rule design, tuning, and performance optimization.
• 2+ years leading or mentoring data scientists or analysts in a technical capacity.
• Demonstrated track record deploying and maintaining models in real‑time production systems.

• Expert-level proficiency with gradient boosting frameworks (XGBoost, Light
  
  GBM, Cat Boost).
• Strong experience with rule engines and decision management systems.
• Advanced feature engineering for transactional and behavioral data.
• Production ML deployment, including model serialization, API integration, and latency optimization.
• Advanced SQL for large‑scale data manipulation (Big Query, Snowflake, or similar).
• Python fluency: pandas, Num Py, scikit‑learn, and model deployment frameworks.
• Experience with model monitoring, drift detection, and automated retraining pipelines.
• Understanding of model explainability techniques (SHAP values, feature importance, gain importance).

• Strong understanding of fraud patterns: account takeover, identity fraud, transaction fraud, or similar.
• Experience designing hybrid systems where models and rules work together effectively.
• Strong grasp of rule lifecycle management: creation, testing, deployment, monitoring, and retirement.
• Familiarity with identity and risk signals (device fingerprinting, phone/email intelligence, velocity patterns).
• Experience balancing approval rates against fraud losses—you understand the business trade‑offs.

• Experience with decisioning platforms (Oscilar, Datavisor, Actimize, or similar).
• Background in money transfer, remittance, or cross‑border payments.
• Experience leading organizations through transitions from rules‑heavy to model‑first fraud detection.

This position will be remotely based in the United…