Fraud Model Developer
SoFi, Frisco, TX, United States
Senior Fraud Model Developer – Fraud Model Development Team
We are looking for a Senior Data Scientist and/or Machine Learning Model Developer to join our Fraud Model Development Team. This role owns end-to-end design, development, validation, deployment partnership, and performance management of high-impact fraud models across SoFi products including Personal Loans, Student Loans, Credit Cards, and Crypto.
The Senior Fraud Model Developer is accountable for measurable fraud loss reduction and false positive improvements within assigned domains. The role requires deep expertise in data analytics, statistical modeling, and machine learning, along with the ability to translate complex model performance into clear business outcomes. The ideal candidate brings strong fraud domain knowledge, production ML experience, and a demonstrated ability to manage model risk and complexity at scale.
What you’ll do
Own end-to-end development of fraud models within assigned product or risk domains, from problem framing through production deployment and ongoing monitoring.
Drive measurable reductions in fraud loss, false positives, and operational expenses.
Translate model outputs into business impact metrics and influence fraud strategy decisions.
Aggregate and synthesize datasets from multiple data environments to design scalable and reusable modeling frameworks.
Analyze complex datasets to identify drivers of fraud loss and member friction across products.
Conduct trade‑off analysis between fraud loss mitigation, customer experience, and regulatory guardrails.
Establish and maintain model monitoring standards (performance metrics, drift detection, recalibration cadence) to proactively manage model risk.
Investigate external risk data and emerging fraud patterns to inform roadmap prioritization.
Partner with ML Platform teams to productionize models in AWS and improve lifecycle governance.
Reduce model development cycle time by simplifying processes, improving documentation rigor, and creating reusable components.
Handle escalations related to model performance, risk exposure, or business impact within assigned scope.
Influence roadmap sequencing and contribute to prioritization discussions based on ROI, regulatory considerations, and level of effort.
Required qualifications
7+ years of advanced quantitative modeling experience, or Master’s degree and 5+ years of related experience, or PhD and 3+ years of related experience, or equivalent practical experience.
Demonstrated ownership of production machine learning models with measurable impact on fraud loss or false positive reduction.
Deep expertise in Python, SQL, and data visualization tools (e.g., Tableau).
Strong knowledge of statistical methodologies and machine learning techniques (regression, decision trees, gradient boosting, random forests, neural networks, clustering analysis).
Experience designing, validating, and monitoring models using metrics such as AUC, KS, precision/recall, and drift detection.
Ability to independently determine modeling approaches, manage trade‑offs, and execute solutions with minimal guidance.
Experience partnering cross‑functionally with Risk, Fraud Ops, Engineering, Finance, and Compliance stakeholders.
Strong communication skills with the ability to distill complex technical concepts into clear business recommendations.
Demonstrated ability to manage risk exposure within projects and proactively elevate with proposed solutions.
Nice to have
Direct experience in fintech, banking, payments, or digital fraud risk.
Familiarity with graph databases and network‑based fraud detection.
Experience developing and deploying models in AWS environments.
Experience influencing fraud policy or risk tolerance decisions.
Compensation and Benefits
The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.
To view all of our comprehensive benefits, please visit our Benefits page.
Equal Employment Opportunity Statement
SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.
The Company hires the best qualified candidate for the job, without regard to protected characteristics.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
New York applicants: Notice of Employee Rights.
Accommodation Statement
SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com.
Location Policy
Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.
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We are looking for a Senior Data Scientist and/or Machine Learning Model Developer to join our Fraud Model Development Team. This role owns end-to-end design, development, validation, deployment partnership, and performance management of high-impact fraud models across SoFi products including Personal Loans, Student Loans, Credit Cards, and Crypto.
The Senior Fraud Model Developer is accountable for measurable fraud loss reduction and false positive improvements within assigned domains. The role requires deep expertise in data analytics, statistical modeling, and machine learning, along with the ability to translate complex model performance into clear business outcomes. The ideal candidate brings strong fraud domain knowledge, production ML experience, and a demonstrated ability to manage model risk and complexity at scale.
What you’ll do
Own end-to-end development of fraud models within assigned product or risk domains, from problem framing through production deployment and ongoing monitoring.
Drive measurable reductions in fraud loss, false positives, and operational expenses.
Translate model outputs into business impact metrics and influence fraud strategy decisions.
Aggregate and synthesize datasets from multiple data environments to design scalable and reusable modeling frameworks.
Analyze complex datasets to identify drivers of fraud loss and member friction across products.
Conduct trade‑off analysis between fraud loss mitigation, customer experience, and regulatory guardrails.
Establish and maintain model monitoring standards (performance metrics, drift detection, recalibration cadence) to proactively manage model risk.
Investigate external risk data and emerging fraud patterns to inform roadmap prioritization.
Partner with ML Platform teams to productionize models in AWS and improve lifecycle governance.
Reduce model development cycle time by simplifying processes, improving documentation rigor, and creating reusable components.
Handle escalations related to model performance, risk exposure, or business impact within assigned scope.
Influence roadmap sequencing and contribute to prioritization discussions based on ROI, regulatory considerations, and level of effort.
Required qualifications
7+ years of advanced quantitative modeling experience, or Master’s degree and 5+ years of related experience, or PhD and 3+ years of related experience, or equivalent practical experience.
Demonstrated ownership of production machine learning models with measurable impact on fraud loss or false positive reduction.
Deep expertise in Python, SQL, and data visualization tools (e.g., Tableau).
Strong knowledge of statistical methodologies and machine learning techniques (regression, decision trees, gradient boosting, random forests, neural networks, clustering analysis).
Experience designing, validating, and monitoring models using metrics such as AUC, KS, precision/recall, and drift detection.
Ability to independently determine modeling approaches, manage trade‑offs, and execute solutions with minimal guidance.
Experience partnering cross‑functionally with Risk, Fraud Ops, Engineering, Finance, and Compliance stakeholders.
Strong communication skills with the ability to distill complex technical concepts into clear business recommendations.
Demonstrated ability to manage risk exposure within projects and proactively elevate with proposed solutions.
Nice to have
Direct experience in fintech, banking, payments, or digital fraud risk.
Familiarity with graph databases and network‑based fraud detection.
Experience developing and deploying models in AWS environments.
Experience influencing fraud policy or risk tolerance decisions.
Compensation and Benefits
The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.
To view all of our comprehensive benefits, please visit our Benefits page.
Equal Employment Opportunity Statement
SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.
The Company hires the best qualified candidate for the job, without regard to protected characteristics.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
New York applicants: Notice of Employee Rights.
Accommodation Statement
SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com.
Location Policy
Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.
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