MoneyGram

Compensation

Full Description

***This role is 100%25 remote and can be located anywhere in the US Did you know there are 1.4 billion1 people in the world that are financially underserved by traditional banks? 🤔 In many cases, people that depend on remittances being sent or received, often across different countries. Sometimes even across different continents. MoneyGram impacts the daily life of 1.5 million customers, connecting families and businesses across borders. By relying on a vast network of agents, by being present in 200+ different countries, and by developing cutting-edge payment technology, MoneyGram is paving the way for global financial fairness and inclusion! Will you join us in our journey? About This Position The role involves developing advanced fraud detection solutions using machine learning and data science techniques. Responsibilities include building models with gradient boosting and exploring deep learning approaches, designing supervised and unsupervised anomaly detection systems, and engineering features from transactional, behavioral, and identity data. The position requires deploying models into real-time production environments with scoring and explainability, conducting champion/challenger experiments, and creating monitoring dashboards for performance, drift detection, and feature stability. The individual will analyze fraud patterns across corridors, customer segments, and transaction types, investigate false positives and negatives, and optimize trade-offs between approval rates and fraud losses. Additional duties include documenting model architecture and performance, supporting data labeling strategies, and communicating insights to both technical and non-technical audiences. What You Will Do: · Build fraud detection models using gradient boosting and experiment with deep learning approaches where appropriate · Build supervised / unsupervised anomaly detection models on labeled and unlabeled data to identify outliers · Engineer features from transaction history, device fingerprints, behavioral signals, and identity data · Deploy models to production with real-time scoring and reason code generation · Design and execute champion/challenger experiments to validate model improvements · Build monitoring dashboards for model performance, drift detection, and feature stability · Analyze fraud patterns by corridor, customer segment, and transaction type to identify modeling opportunities · Investigate false positives and false negatives to drive continuous model improvement · Quantify trade-offs between approval rates and fraud losses at different threshold levels · Document model architecture, feature definitions, and performance characteristics · Support data labeling strategy and quality assurance with operations teams · Communicate insights and recommendations clearly to both technical and non-technical audiences Who We’re Looking For: Experience · 4+ years of experience in machine learning and data science · 2+ years building production ML models in fraud, risk, payments, or financial services · Proven experience deploying and maintaining models in real-time production systems Technical Skills · Strong proficiency with gradient boosting frameworks (XGBoost, LightGBM, CatBoost) · Solid feature engineering skills—ability to extract signal from transactional and behavioral data · Production ML experience including model serialization, deployment, and performance monitoring · Proficient SQL for working with large datasets (BigQuery, Snowflake, or similar) · Proficiency in Python: pandas, NumPy, scikit-learn, and familiarity with deployment tools · Understanding of model explainability (SHAP values, feature importance, gain importance) · Experience with A/B testing or champion/challenger experimental design Domain Knowledge · Understanding of fraud detection concepts: false positive/negative trade-offs, precision/recall, threshold optimization · Familiarity with common fraud signals (velocity, device, identity, behavioral) · Ability to translate model outputs into business impact (approval rates, loss rates, customer friction) Preferred · Experience with payment fraud, account takeover, or identity fraud specifically · Familiarity with identity verification signals (device fingerprinting, phone/email risk) · Experience with decisioning platforms (Oscilar, Datavisor, Actimize, or similar) · Background in anomaly detection or unsupervised learning for emerging fraud patterns Location: · This position will be remotely based in the United States Here Are Some Reasons You Will Love Working At MoneyGram! · Remote first flexibility · Generous PTO · 13 Paid Holidays · Medical / Dental / Vision Insurance · Life, Disability, and other benefits · 401k with competitive Employer Match · Community Service Days · Generous Parental Leave Anticipated Base Pay: $130,000 – $185,000 + participation in our annual bonus plan. The salary/pay rate listed is a good faith determination that may be offered to a successful applicant for this position at the time of this job advertisement based on company hiring process and budget for this role and may be modified in the future. Actual compensation may vary from posting based on geographic location, work experience, education and/or skill level. MoneyGram does not sponsor US work authorizations for this job position including H-1B, O-1, and TN. MoneyGram also does not hire F-1’s working on EAD for this position. About MoneyGram MoneyGram International, Inc. is a global financial technology leader, empowering consumers and businesses to send and manage money across over 200 countries and territories. With an industry-leading app and one of the world’s largest cash distribution networks, MoneyGram processes more than $200 billion annually, serving over 50 million people. A pioneer in blockchain technology, the company enables customers to buy, sell, and hold digital currencies, with over 50%25 of transactions now digital. Headquartered in Dallas, Texas, MoneyGram is celebrated for its strong culture, earning the Top Workplaces USA award three years in a row. Date Science