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Threat Intelligence: AML Strategic Analytic Innovation

Employee Applicant Privacy NoticeWho we are: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 financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re 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 Threat Intelligence: AML strategic analytic innovation role will be responsible for optimizing the AML program through cutting-edge analytic tools and techniques. In this position, you will support the AML department by conducting in-depth data analysis, pulling key insights, and leveraging advanced technologies such as large language models (LLMs) or robotic process automation (RPA) to drive efficiency, improve threat detection capabilities, and streamline investigative workflows. Additionally, you will be responsible for developing and integrating LLM-driven solutions into business processes, enhancing automation, and improving AML monitoring effectiveness.What you’ll do:• Build experimental models utilizing machine learning and statistical modeling methods for supervised and unsupervised learning.• Use advanced data mining techniques to identify patterns, anomalies, and potential red flags across large datasets.• Contribute to the continuous optimization of the AML program by identifying opportunities for improvement in processes, tools, and workflows.• Design, develop, and implement automated solutions to enhance AML investigative workflows, reducing manual effort and improving detection efficiency.• Leverage large language models (LLMs) to optimize typology identification, suspicious activity reporting, and risk assessments, ensuring greater consistency and accuracy in AML processes.• Integrate LLM-based tools into existing business systems to automate report generation, transaction monitoring, and case review processes.• Collaborate with external technology service providers (TSPs) and industry groups to stay updated on the latest system/process innovations.• Support integration of new TSPs into existing monitoring and surveillance tools.• Work with technical teams to integrate new analytical techniques into existing systems, improving efficiency and predictive capabilities.• Develop prompt engineering strategies to fine-tune LLM outputs for AML-specific use cases, ensuring optimal performance and reducing false positives.• Provide regular updates on threat intelligence findings, ensuring all stakeholders are aware of the evolving risks and necessary responses.• Research ongoing Machine Learning and Artificial Intelligence trends and products.• Document and refine integration methodologies to support scalability, reproducibility, and compliance with model risk management requirements.• Develop reports and presentations on threat intelligence findings, ensuring that they are clear, actionable, and tailored to different audiences, including senior leadership and regulatory bodies.What you’ll need:• Bachelor’s Degree or Master’s Degree in Statistics, Computer Science, Mathematics, Finance, Engineering, or other relevant fields.• 7+ years of experience in the finance industry focusing on BSA/AML, OFAC, or fraud modeling/analytics, with a demonstrated ability to build and deploy AI/LLM-driven solutions in a business environment.• Hands-on experience implementing LLMs in AML or financial crime detection, with a strong understanding of their capabilities and limitations.• Proficiency in designing, fine-tuning, and integrating LLMs into transaction monitoring, adverse media screening, or suspicious activity reporting workflows.• Experience working with AI/ML models in cloud environments and integrating them into enterprise systems.• Statistical/data analytical skills, including data quality validation, and predictive modeling experience in SQL, R and/or Python.• Knowledge of and ability to leverage traditional databases, cloud-based computing, and distributed computing.• Proficiency in data analysis and investigative tools, with experience pulling, analyzing, and visualizing large datasets from various sources.• Strong problem-solving skills, with the ability to distill complex data into clear, actionable intelligence.• Excellent written and verbal communication skills, with the ability to present findings and insights to both technical and non-technical stakeholders.• A proactive approach to learning and staying up-to-date on industry trends, emerging threats, and innovative technologies.• Knowledge of AML regulations and the USA PATRIOT Act.• Familiarity with regulatory guidance on Model Risk Management (Federal Reserve SR Letter 11-7, OCC Bulletin 2011-12, FDIC FIL 22-2017, DFS504)• Experience with data visualization (e.g., Tableau)• Experience with cloud data infrastructure (e.g., Snowflake)• Experience with automated transaction monitoring (e.g., Verafin)• Experience with customer/transaction screening (e.g., LexisNexis)• CAMS certification preferredCompensation And BenefitsThe 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 and competitive benefits, visit our Benefits at SoFi page!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 RightsSoFi is committed to embracing diversity. 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.Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.Internal EmployeesIf you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.

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What You Should Know About Threat Intelligence: AML Strategic Analytic Innovation, SoFi

Join SoFi as a Threat Intelligence: AML Strategic Analytic Innovation expert in Frisco, TX, where you will play a crucial role in transforming the ways financial intelligence is harnessed. In this dynamic position, you will leverage innovative analytic tools and cutting-edge technologies like large language models (LLMs) and robotic process automation (RPA) to enhance the Anti-Money Laundering (AML) program. You will dive deep into data analysis to unearth critical insights, optimize investigative workflows, and elevate threat detection capabilities. Collaborating with other technical teams, you’ll design automated solutions that streamline processes and help identify patterns and anomalies across vast datasets. Your experience will contribute to the continuous enhancement of AML monitoring effectiveness and align with industry trends. Daily tasks will challenge you to build experimental models using machine learning, improve existing workflows, and develop strategies that integrate advanced technologies into our systems. As you execute these responsibilities, you will also craft reports tailored for different stakeholders, ensuring everyone understands evolving risks and the necessary responses. If you’re proficient in data analysis using SQL, R, and Python, and have a firm grasp of AML regulations and technologies, this is your chance to make a meaningful impact while working alongside a forward-thinking team at SoFi.

Frequently Asked Questions (FAQs) for Threat Intelligence: AML Strategic Analytic Innovation Role at SoFi
What are the key responsibilities of the Threat Intelligence: AML Strategic Analytic Innovation position at SoFi?

The Threat Intelligence: AML Strategic Analytic Innovation role at SoFi involves optimizing the AML program through advanced analytics. Key responsibilities include conducting in-depth data analyses, building machine learning models, integrating large language models into workflows, and improving overall detection efficiency. You will also work closely with technical teams to enhance investigative processes and ensure effective AML monitoring.

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What qualifications are required for the Threat Intelligence role at SoFi?

Candidates for the Threat Intelligence: AML Strategic Analytic Innovation role at SoFi should possess a Bachelor's or Master's degree in relevant fields such as Statistics or Computer Science. Additionally, having over 7 years of experience in the finance industry, particularly in BSA/AML or fraud analytics, is essential. Candidates should also demonstrate hands-on experience implementing AI/LLM solutions in AML processes.

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How does the role of Threat Intelligence: AML Strategic Analytic Innovation contribute to SoFi’s AML program?

The Threat Intelligence: AML Strategic Analytic Innovation role significantly enhances SoFi's AML program by employing data-driven insights and advanced technologies to improve monitoring and detection of suspicious activities. By developing automated solutions and integrating LLMs, the role aims to streamline workflows, reduce manual efforts, and increase the accuracy of reporting and compliance processes.

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What tools and technologies will I be using in the Threat Intelligence role at SoFi?

In the Threat Intelligence: AML Strategic Analytic Innovation role at SoFi, you will work with a variety of advanced tools and technologies, including machine learning frameworks, data visualization software like Tableau, cloud data infrastructures such as Snowflake, and automation tools designed for transaction monitoring and suspicious activity reporting.

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Is experience with large language models required for the Threat Intelligence role at SoFi?

Yes, experience with large language models is a vital requirement for the Threat Intelligence: AML Strategic Analytic Innovation position at SoFi. Candidates should be proficient in designing and integrating LLMs for transaction monitoring and understanding their capabilities within the AML framework to fully leverage their potential in enhancing analytical processes.

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Common Interview Questions for Threat Intelligence: AML Strategic Analytic Innovation
Can you explain how you would approach developing a machine learning model for AML detection?

In answering this question, you should outline your methodology starting from understanding the data requirements, preprocessing the data for accuracy, choosing the right algorithm based on the dataset characteristics, and continuously evaluating the model’s performance. Emphasize the importance of aligning the model with regulatory requirements and AML best practices.

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Describe a significant challenge you faced in a past analytical project and how you overcame it.

In your response, focus on a specific project where you encountered unexpected data anomalies or integration issues. Discuss the steps you took to identify the issue, the analytical tools you used, and how you collaborated with your team to implement a solution that ensured the project met its objectives.

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How do you stay updated on trends in machine learning and AML regulations?

Discuss resources you utilize to keep abreast of emerging technologies, such as attending industry conferences, following influential experts online, and participating in relevant training sessions. Highlight any specific forums or journals you regularly read for updates on AML regulations and machine learning advancements.

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What strategies would you implement to improve the effectiveness of AML monitoring at SoFi?

Your strategy should encompass the integration of advanced data analytics tools, the use of predictive modeling to forecast risks, and training staff to ensure they can utilize these tools effectively. Emphasize the importance of iterative testing and optimization to enhance monitoring systems continuously.

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What experience do you have with cloud computing and its application in AML processes?

Here, emphasize your practical experiences with cloud platforms. Discuss how you have leveraged cloud computing to handle large datasets and implement scalable solutions that enhance AML operations, particularly in transaction monitoring and data analysis.

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How do you ensure compliance with AML regulations in your analytical work?

Explain that compliance involves continuously reviewing existing regulations and aligning analytical practices with these standards. Talk about your experience in implementing systems that ensure reporting accuracy and the role of regular audits in maintaining compliance.

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Can you give an example of how you’ve used predictive modeling in your previous roles?

Provide a specific example where you successfully used predictive modeling to identify potential fraud or suspicious transactions. Highlight the techniques you used, the data you analyzed, and how your findings influenced decision-making in the organization.

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Explain your experience with integrating advanced analytical techniques into existing systems.

Discuss any specific projects where you integrated new analytical tools or systems into an organization’s existing infrastructure. Highlight the challenges faced during this integration and how you worked with cross-functional teams to ensure a smooth transition.

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What role do you think data visualization plays in AML analytics?

Illustrate how data visualization transforms raw data into actionable insights, making complex information easier for stakeholders to understand. Discuss tools you’ve utilized for visualization and their impact on decision-making processes.

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How would you handle a situation where your findings on potential suspicious activity were disputed by a colleague?

Discuss the importance of collaboration and communication in such situations. Emphasize approaching the discussion with an open mind, providing evidence for your findings, and being willing to engage in a constructive dialogue to reach a consensus.

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Full-time, on-site
DATE POSTED
March 21, 2025

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