Apply at MoneyGram
United States
$150k - $200k
<span id="requisitionDescriptionInterface.reqTitleLinkAction.row1" title="">Manager, Data Science - US Remote</span><span> </span><span id="requisitionDescriptionInterface.ID1455.row1" title="">-</span><span> </span><span id="requisitionDescriptionInterface.ID1465.row1" title="">(</span><span id="requisitionDescriptionInterface.reqContestNumberValue.row1" title="">26010101</span><span id="requisitionDescriptionInterface.ID1479.row1" title="">)</span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span>Description</span></h2><span><span> </span></span><span id="requisitionDescriptionInterface.ID1529.row1" title=""><p><span><span>***This role is 100% remote and can be located anywhere in the US</span></span></p><p><span><span lang="EN-GB">Did you know there are 1.4 billion1 people in the world that are financially underserved by traditional banks? </span></span><span><span lang="EN-GB">🤔</span></span><span><span> </span></span></p><p><span><span> </span><span lang="EN-GB">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.</span><span> </span></span></p><p><br/><span><span lang="EN-GB">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!</span><span> </span></span></p><p><span><span> </span><span lang="EN-GB">Will you join us in our journey?</span><span> </span></span></p><p><span><span><strong>About the Manager, Data Scientist</strong></span></span></p><p><span><span><strong> </strong></span></span></p><p><span><span>The role is responsible for designing, developing, and deploying production fraud detection models that score transactions in real time. This individual will co-own the end-to-end data science roadmap for transaction fraud and risk, build and maintain feature pipelines leveraging transaction data, device signals, behavioral patterns, and identity attributes, and lead the transition from rules-based fraud detection to a model-first decisioning architecture. They will design the interaction between models and rules, implement champion/challenger frameworks to optimize performance, and create monitoring systems for model drift, feature distribution shifts, and rule effectiveness. Additional responsibilities include generating explainability outputs for every model decision, mentoring a small team of data scientists while remaining hands-on, partnering with Risk Intelligence to align strategies with business objectives, and presenting performance analysis and strategic recommendations to leadership. </span><span></span></span></p><p><span><span lang="EN-GB"> </span></span><span><span lang="EN-GB"></span></span></p><p><span><span><strong>What You Will Do:</strong></span></span><span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Design, develop, and deploy production fraud detection models that score transactions in real-time</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Co-own the end-to-end data science roadmap for transaction fraud and risk</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Build and maintain feature pipelines using transaction data, device signals, behavioral patterns, and identity attributes </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Lead the transition from rules-based fraud detection to model-first decisioning architecture</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Design the interaction between models and rules; determining when models make primary decisions versus rules </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Implement champion/challenger frameworks to continuously test and improve both model and rule performance</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Create monitoring systems for model drift, feature distribution shifts, rule effectiveness, and overall system performance</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Generate reason codes and explainability outputs for every model decision</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Mentor and lead a small team of data scientists while remaining hands-on with development </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Partner with Risk Intelligence team to align model and rule strategies with business objectives</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Present performance analysis, trade-off recommendations, and strategic roadmaps to leadership</span><span> </span></span></p><p><span><span><strong> </strong></span></span></p><p><span><span><strong>Who We’re Looking For:</strong></span><span> </span></span><span></span></p><p><span><span lang="EN"><strong>Experience</strong></span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">7+ years of progressive experience in machine learning, data science, or quantitative risk</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">4+ years building production ML models in fraud, risk, payments, or financial services</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">3+ years working with rule-based fraud detection systems, including rule design, tuning, and performance optimization</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">2+ years leading or mentoring data scientists or analysts in a technical capacity</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Demonstrated track record deploying and maintaining models in real-time production systems </span></span></p><p><span><span lang="EN"><strong>Technical Skills</strong></span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Expert-level proficiency with gradient boosting frameworks (XGBoost, LightGBM, CatBoost) </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Strong experience with rule engines and decision management systems</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Advanced feature engineering for transactional and behavioral data</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Production ML deployment including model serialization, API integration, and latency optimization</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Advanced SQL for large-scale data manipulation (BigQuery, Snowflake, or similar) </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Python fluency: pandas, NumPy, scikit-learn, and model deployment frameworks</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Experience with model monitoring, drift detection, and automated retraining pipelines</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Understanding of model explainability techniques (SHAP values, feature importance, gain importance)</span><span> </span></span></p><p><span><span lang="EN"><strong>Domain Knowledge</strong></span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Strong understanding of fraud patterns: account takeover, identity fraud, transaction fraud, or similar</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Experience designing hybrid systems where models and rules work together effectively</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Strong grasp of rule lifecycle management: creation, testing, deployment, monitoring, and retirement</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Familiarity with identity and risk signals (device fingerprinting, phone/email intelligence, velocity patterns)</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Experience balancing approval rates against fraud losses—you understand the business trade-offs</span><span> </span></span></p><p><span><span lang="EN"><strong>Preferred</strong></span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span>Experience with decisioning platforms (Oscilar, Datavisor, Actimize, or similar) </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Background in money transfer, remittance, or cross-border payments</span><span> </span></span></p><p><span><span>·</span><span> </span></span><span><span lang="EN">Experience leading organizations through transitions from rules-heavy to model-first fraud detection</span><span> </span><span></span></span></p><p><span><span><strong>Location:</strong></span></span></p><p><span><span>·</span><span> </span></span><span><span>This position will be remotely based in the United States</span></span></p><p><span><span><strong>Here Are Some Reasons You Will Love Working At MoneyGram!</strong></span></span></p><p><span><span>·</span><span> </span></span><span><span>Remote first flexibility</span></span></p><p><span><span>·</span><span> </span></span><span><span>Generous PTO</span></span></p><p><span><span>·</span><span> </span></span><span><span>13 Paid Holidays</span></span></p><p><span><span>·</span><span> </span></span><span><span>Medical / Dental / Vision Insurance</span></span></p><p><span><span>·</span><span> </span></span><span><span>Life, Disability, and other benefits</span></span></p><p><span><span>·</span><span> </span></span><span><span>401k with competitive Employer Match</span></span></p><p><span><span>·</span><span> </span></span><span><span>Community Service Days</span></span></p><p><span><span>·</span><span> </span></span><span><span>Generous Parental Leave</span></span></p><p><span><span>Anticipated Base Pay: $150,000 – $200,000 + participation in our annual bonus plan. </span></span></p><p><span><i><span>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. </span></i><span></span></span></p><p><span><span>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. </span></span></p><p><span><span><strong>About MoneyGram</strong> </span></span></p><p><span><span>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% 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. </span></span></p></span><span><span> </span></span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span>Qualifications</span></h2><span><span> </span></span><span id="requisitionDescriptionInterface.ID1587.row1" title=""><p><span><span>Date Science</span></span></p></span><span><span> </span></span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span id="requisitionDescriptionInterface.ID1618.row1" title="">Primary Location</span></h2><span id="requisitionDescriptionInterface.ID1635.row1" title="">:</span><span> </span><span id="requisitionDescriptionInterface.ID1645.row1" title="">United States of America-New York-New York</span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span id="requisitionDescriptionInterface.ID1718.row1" title="">Work Locations</span></h2><span id="requisitionDescriptionInterface.ID1735.row1" title="">:</span><span> </span><span id="requisitionDescriptionInterface.reqSiteName.row1" title="">Virtual-NewYork</span><span> </span><span id="requisitionDescriptionInterface.reqSiteAddressLine1.row1" title=""></span><span> </span><span id="requisitionDescriptionInterface.reqSiteAddressLine2.row1" title=""></span><span> </span><span id="requisitionDescriptionInterface.reqSiteCity.row1" title=""></span><span> </span><span id="requisitionDescriptionInterface.reqSiteZipCode.row1" title=""></span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span id="requisitionDescriptionInterface.ID1830.row1" title="">Job</span></h2><span id="requisitionDescriptionInterface.ID1847.row1" title="">:</span><span> </span><span id="requisitionDescriptionInterface.ID1857.row1" title="">Digital MGO</span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span id="requisitionDescriptionInterface.ID1880.row1" title="">Organization</span></h2><span id="requisitionDescriptionInterface.ID1897.row1" title="">:</span><span> </span><span id="requisitionDescriptionInterface.ID1907.row1" title="">Digital</span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"></h2><span id="requisitionDescriptionInterface.ID2075.row1" title="">:</span><span> </span><span id="requisitionDescriptionInterface.ID2085.row1" title="">Full-time</span><h2 xmlns:ftl="http://www.taleo.net/ftl" xmlns:htm="http://www.w3.org/1999/xhtml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><span id="requisitionDescriptionInterface.ID2208.row1" title="">Job Posting</span></h2><span id="requisitionDescriptionInterface.ID2225.row1" title="">:</span><span> </span><span id="requisitionDescriptionInterface.reqPostingDate.row1" title="">Jan 2, 2026, 7:44:21 PM</span>
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