Senior Manager, Data Science

Portfolio Management and Loss Mitigation

The Role: 

SoFi is seeking an inspirational and deeply experienced Senior Manager to lead the Portfolio Management and Loss Mitigation function in our Risk Data Science team. Reporting to the Head of Risk Data Science, this role will lead the development, deployment, and governance of portfolio management and loss mitigation models for all credit products.

The ideal candidate is a hands-on leader who can transition the team from traditional modeling to next-generation machine learning platforms, leveraging emerging data sources (e.g., cash flow, alternative bureaus) to significantly improve portfolio performance, reduce losses, and ensure rigorous adherence to Model Risk Management (MRM) standards. This role requires exceptional organizational leadership, an ability to influence stakeholders, and proven success in delivering complex models into a regulated production environment.

What You'll Do: 

• Portfolio Management and Loss Mitigation Excellence: Directly oversee the development and deployment of Next Generation models designed to manage and mitigate portfolio losses while maintaining loss guardrails.
• Drive Next-Generation Capabilities: Incorporate industry trends and advanced techniques (NLP, Graph Mining, LLMs, Deep Learning) to solve complex, high-impact risk problems where established principles may not fully apply.
• Alternative Data Strategy: Spearhead the evaluation and integration of alternative data sources (Macro-Economic variables, tri-bureau, LexisNexis, cash flow data) to enhance predictive power across all credit products.
• Lead the current team of high-performing Staff and Senior Data Scientists. Recruit, mentor, and foster talent through deliberate interactions, succession planning, and creating a high-accountability, low-ego culture.
• Model Risk Management (MRM): Act as the primary owner for all models in the portfolio, ensuring robust documentation, monitoring, and successfully navigating the 2nd Line of Defense (2LOD) review and approval process (SR 11-7 familiarity is mandatory).
• Stakeholder Alignment: Interact and negotiate with senior management and external stakeholders to reconcile competing views and drive critical, high-impact business decisions.
• Automation and Efficiency: Lead efforts to automate model monitoring and governance processes to create scalable and auditable infrastructure.

What You'll Need:

• Experience: 8+ years of progressive experience in credit risk, modeling, and data science within a regulated financial institution (FinTech, Bank, or similar), with at least 3 years in a people management role, managing technical staff.
• Education: Master’s or Ph.D. degree in a quantitative field (Statistics, Computer Science, Engineering, Operations Research, etc.).
• Technical Acumen: Deep expertise in advanced statistical and machine learning modeling techniques (e.g., Gradient Boosting, Deep Learning, Causal Inference).
• Regulatory Knowledge: Detailed working knowledge of model risk management standards (e.g., SR 11-7) and the ability to operate within a highly regulated environment.
• Tools & Platforms: Expert-level proficiency in Python (PySpark, scikit-learn, TensorFlow/PyTorch) and SQL/data warehouse technologies (e.g., Snowflake, Hive). Familiarity with modern MLOps platforms and cloud computing (AWS).
• 3+ years of progressive people management experience is required, including the ability to recruit, mentor, and foster talent within a team of high-performing Staff and Senior Data Scientists.
• Communication: Ability to distill highly complex analytical concepts into clear, concise, and compelling narratives for non-technical leadership.

Shape a brighter financial future with us.

Together with our members, we’re changing the way people think about and interact with personal finance.
We’re a next-generation fintech company using innovative, mobile-first technology to help our millions of members
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proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values
guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.

The Role

The Fraud Program is seeking a Staff Risk Analyst to lead data governance initiatives, analytics, and reporting across
the Fraud Center of Excellence (CoE). This role will serve as a key partner in developing, maintaining, and enhancing
fraud KPIs, KRIs, dashboards, and enterprise risk reporting to enable data-driven decision making and effective fraud
risk oversight.

The Staff Risk Analyst will play a critical role in ensuring data integrity, consistency, and transparency across fraud
metrics, while delivering actionable insights into fraud performance, emerging risks, and operational effectiveness
across all products and fraud domains.

This role will partner closely with Fraud Strategy, Operations, Risk, Data, and Business Intelligence teams to build
scalable analytics solutions, standardize metric definitions, and improve enterprise-wide visibility into fraud risk.
The ideal candidate will bring a strong technical data background, experience in risk analytics, and a passion for
building scalable data and reporting frameworks within a fast-paced fintech environment. Additionally, the
candidate will also have strong proficiency in SQL, Python, Snowflake, and Tableau. You’ll need to be a self-motivated
leader with the ability to drive cross-functional collaboration between all lines of business. IIf you love working with
data and have a passion for doing the right thing, we want to hear from you!

What you’ll do:

Fraud Data Analytics, Insights, and Data Governance (Primary Focus)
● Analyze fraud data across products and channels to identify trends, anomalies, and emerging fraud risks.
● Develop data-driven insights and recommendations to support fraud strategy, operations, and risk
management decisions.
● Partner with cross-functional teams to translate business problems into analytical solutions that improve
fraud detection, prevention, and member experience.
● Establish and enforce data governance standards for fraud metrics- including data definitions, lineage, and
controls; and effectively leading a strategic data governance council cross-functionally.
● Identify and resolve data quality, consistency, and availability issues impacting fraud reporting and
analytics.
● Partner with Data Engineering and BI teams to ensure reliable, scalable, and well-documented data
sources.
KPI / KRI Framework & Performance Monitoring
● Support the design, development, and ongoing enhancement of Fraud KPIs, KRIs, and Risk Appetite

Statement (RAS) metrics across the Fraud CoE.

● Ensure consistent metric definitions, calculations, and reporting methodologies across business units.
● Monitor metric performance and provide insights into drivers of variance, trends, and risk signals.
Dashboarding & Reporting
● Assist in designing, building, and maintaining Tableau dashboards and automated reporting solutions to
provide real-time visibility into fraud performance.
● Deliver executive-level reporting and visualizations for fraud governance committees and leadership.
● Improve reporting efficiency through automation and scalable data pipelines (e.g., Snowflake), where
needed, in partnership with our risk analytics and data teams
Fraud Risk Management & Assessment Support
● Support Fraud Risk Self-Assessments (FRSA) and other risk evaluation activities through data analysis and
metric development.
● Assist in identifying key risk drivers and areas of elevated fraud exposure across products and processes.
● Provide analytical support for audit, regulatory, and risk management requests.
Cross-Functional Collaboration & Program Support
● Collaborate with Fraud Strategy, Operations, and Program teams to align data, metrics, and reporting with
business needs.
● Support broader Fraud Program initiatives and analytics projects as needed, including strategy
performance analysis and operational insights.

What you’ll need:

● 4+ years of experience in data analytics, fraud analytics, or risk analytics within financial services, fintech, or
banking.
● Strong experience working with large datasets and relational databases.
● Advanced proficiency in SQL and experience with Snowflake or similar data platforms.
● Experience building dashboards and data visualizations (Tableau preferred).
● Strong understanding of KPI/KRI frameworks, performance metrics, and risk monitoring concepts.
● Experience with data governance, data quality management, or metric standardization.
● Strong analytical and problem-solving skills with the ability to translate complex data into actionable
insights.
● Excellent communication skills, with the ability to present findings to both technical and non-technical
stakeholders.
● Ability to work cross-functionally and manage multiple priorities in a fast-paced environment.
● Proficient in other programming and data analysis tools such as Python, R, or SAS.

Nice to Have:
● Advanced degree in Data Science or Machine Learning.
● Experience in implementing or overseeing AI/ML-based models for fraud detection
● Experience working in or working closely with model risk management is a plus.

The role:

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. This individual partners closely with Fraud Prevention, Risk, Operations, Finance, Compliance, and ML Platform teams to influence fraud strategy, inform risk tolerance decisions, and ensure models deliver sustained business impact.

This position 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:

The Senior Fraud Model Developer will help SoFi build and scale high-performing fraud modeling solutions by:

• Owning end-to-end development of fraud models within assigned product or risk domains, from problem framing through production deployment and ongoing monitoring
• Driving measurable reductions in fraud loss, false positives, and operational expenses
• Translating model outputs into business impact metrics and influencing fraud strategy decisions
• Aggregating and synthesizing datasets from multiple data environments to design scalable and reusable modeling frameworks
• Analyzing complex datasets to identify drivers of fraud loss and member friction across products
• Conducting trade-off analysis between fraud loss mitigation, customer experience, and regulatory guardrails
• Establishing and maintaining model monitoring standards (e.g., performance metrics, drift detection, recalibration cadence) to proactively manage model risk
• Investigating external risk data and emerging fraud patterns to inform roadmap prioritization
• Partnering with ML Platform teams to productionize models in AWS and improve lifecycle governance
• Reducing model development cycle time by simplifying processes, improving documentation rigor, and creating reusable components
• Handling escalations related to model performance, risk exposure, or business impact within assigned scope
• Influencing roadmap sequencing and contributing to prioritization discussions based on ROI, regulatory considerations, and level of effort

What you’ll need:

• 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 (e.g., 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 escalate 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