Stripe

<h2><strong>Who we are</strong></h2>
<h3><strong>About Stripe </strong></h3>
<p>Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.</p>
<h2><strong>What you’ll do</strong></h2>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Analyze accounts and transactions to identify and predict risky behavior that could result in losses for Stripe or pose challenges to the broader payments ecosystem. </li>
<li>Scale risk processes to cover an increasingly large and complex landscape of payments risks and effectively design strategies and solutions that might range from ML model features to manual review workflows. </li>
<li>Work with internal partners like Engineering, Product, Data Science and Operations to develop solutions that would mitigate fraud risk across different geographies, payment methods and products. </li>
<li>Work with Stripe users to educate them about and help prevent potential risks to their business. </li>
<li>Challenge the payments industry status quo to help enable innovative businesses to flourish online.</li>
</ul>
<h2><strong>Who you are</strong></h2>
<h3><strong>Minimum requirements</strong></h3>
<p>Must have a Bachelor's degree or foreign equivalent in Business Administration, Economics, Statistics, Finance, Engineering, or a related quantitative field plus 4 years of experience as a Risk or Fraud Strategist or a related occupation.</p>
<p>Must also have 3 years of experience in each of the following:</p>
<ul>
<li>Payments-related domains, including regional payment methods, risk management, fraud prevention, or trust and safety operations;</li>
<li>Working with quantitative tools including Python, R or Stata;</li>
<li>Using SQL;</li>
<li>Project Management analytical tools including Jira, Asana, and Gantt charts;</li>
<li>Predictive Analytics;</li>
<li>Machine Learning;</li>
<li>A/B Testing; and</li>
<li>Databricks and Airflow.</li>
</ul>
<p>Salary: $121,600.00 - $182,400.00 /yr.  </p>
<p>This salary range represents the base salary range for the role and any sales commissions/sales bonuses targets, if applicable, would be in addition to the base salary.</p>
<p>40 hrs/week</p>
<p>100 % Telecommuting permitted</p>
<p>Multiple Positions Available. </p>
<p>Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.<strong>CA62</strong></p>
<h3><strong>#LI-DNI</strong></h3>

Credit Risk, Fraud Strategist

<h2><span>Who we are</span></h2>
<h3><span>About Stripe</span></h3>
<p><span>Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.</span></p>
<h3><span>About the team</span></h3>
<p>The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe.</p>
<p>The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods. </p>
<h2><span>What you’ll do</span></h2>
<p>You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance.</p>
<p>Your work may include developing transformer architectures for payment data, building graph-powered intelligence across consumers, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections with frontier AI labs. </p>
<p>You will have opportunities to ship 0→1 products, and to improve mission-critical ML models operating at Stripe scale. </p>
<h3><span>Responsibilities</span></h3>
<ul>
<li>Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network.</li>
<li>Investigate emerging fraud and abuse patterns, identify where ML can help, and rapidly develop effective solutions.</li>
<li>Design features, datasets, evaluation methods, and experiments for noisy, delayed, and adversarial environments.</li>
<li>Improve model architectures, training methods, and production systems to increase model quality, coverage, and adaptability.</li>
<li>Monitor production behavior and improve models as data, customer needs, and attack patterns change.</li>
<li>Balance model quality with legitimate-user impact, latency, reliability, and operational complexity.</li>
<li>Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.</li>
<li>Contribute to reusable tools and AI-assisted workflows that help the team develop and operate ML systems more effectively.</li>
</ul>
<h2><span><strong>Who you are</strong></span></h2>
<p>You are a fast, pragmatic problem solver with deep ML expertise and a bias toward action. You are excited to work at the frontier of payments and AI - turning advances in machine learning into production systems that address new forms of fraud and abuse. You’ve built ML models and shepherded them to production, landing impact for users.</p>
<p>We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.</p>
<h3><span>Minimum requirements</span></h3>
<ul>
<li>2+ years of experience developing, evaluating, and deploying machine learning models in a production environment.</li>
<li>Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.</li>
<li>Strong programming and software-engineering skills, with experience writing tested, maintainable code for production ML applications.</li>
<li>Experience building or operating production ML systems, including one or more of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.</li>
<li>Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.</li>
<li>Ability to communicate technical ideas and results clearly and collaborate effectively across disciplines.</li>
</ul>
<h3><span><strong>Preferred qualifications</strong></span></h3>
<ul>
<li>Experience owning an ML problem across the full lifecycle, from problem framing and data exploration through deployment, monitoring, and iteration.</li>
<li>Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches.</li>
<li>Experience building and optimizing real-time, low-latency ML systems at scale.</li>
<li>Experience applying ML to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.</li>
<li>Experience rapidly building 0→1 products, working directly with customers, or operating effectively in a fast-moving environment.</li>
<li>Active interest in the latest ML developments, and how they can be leveraged to solve business problems.</li>
</ul>

Machine Learning Engineer, Radar

<h2><strong>Who we are</strong></h2>
<h3><strong>About Stripe</strong></h3>
<p>Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.</p>
<h3><strong>About the team</strong></h3>
<p>The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe.</p>
<p>The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods. </p>
<h2><strong>What you’ll do</strong></h2>
<p>You will lead the machine learning strategy for abuse prevention across the full customer lifecycle. Abuse doesn't stop at the payment. Bad actors create fake and duplicate accounts, take over legitimate ones, farm free trials and promotions, resell access they obtained below cost, use services they never intend to pay for, and file bad-faith refunds and disputes. For AI companies, every abusive account burns real compute, and the same actors keep moving from one account, product, and access path to the next.</p>
<p>Stripe is uniquely positioned to solve this. Our network sees consumers across millions of businesses, so we can recognize a bad actor at signup, at login, at payment, and long after. Radar has already shipped the first building blocks: account evaluations at signup and login, customer-level abuse risk, and APIs that let businesses report their own abuse outcomes. You will turn these building blocks into a unified abuse prevention layer that understands the consumer behind every event and helps businesses act consistently across all of that consumer's accounts. This is a large growth opportunity for Radar, and a strategic priority for Stripe.</p>
<p>This is a fast-moving 0→1 area, built alongside some of the world's leading AI companies, and the attacks are real today. Labels are sparse, delayed, and differ by business. Much of the customer journey is only partly visible to us. Adversaries adapt as soon as we ship. You will set the technical direction, make the hard architecture calls, and personally build the hardest pieces.</p>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Set the technical strategy and multi-quarter roadmap for lifecycle abuse ML, with product and engineering leadership: which abuse vectors to take on, in what order, and with what approach.</li>
<li>Design a persistent, continuously updated consumer risk profile that combines signals from signups, logins, payments, product usage, refunds, and disputes. Evolve it from explainable composite scores toward model-driven risk scores for each type of abuse.</li>
<li>Lead development of models for abuse vectors such as multi-accounting, account takeover, bots, free-trial and promotion abuse, unauthorized resale, non-payment, refund abuse, and friendly fraud. Use graph, sequence, and LLM-based approaches over payment, account, and product-usage data.</li>
<li>Solve the label problem by designing ways to learn from sparse, delayed, merchant-specific, and weak labels, including merchant-shared feedback, LLM-assisted labeling, and network-level signals.</li>
<li>Define how we measure success: evaluation frameworks, impact and ROI estimates, and operating-point guidance that help businesses trade off abuse losses against growth and conversion.</li>
<li>Turn detection into action by shaping the risk controls, interventions, and recommendations businesses can turn on at each step of the customer journey.</li>
<li>Stay deeply hands-on: write substantial production code, prototype quickly, and personally land the most technically difficult components, with the reliability and latency standards of systems in Stripe's critical path.</li>
<li>Break ambiguous problems into parallel workstreams, unblock other engineers, and raise the team's bar through design review, code review, and mentorship.</li>
<li>Partner directly with leading Stripe users, fraud experts, product, and adjacent Stripe teams to ground the work in real user pain and change course quickly based on what you learn.</li>
<li>Build AI-native tooling, agents, and workflows that let the team investigate attacks and build and operate models dramatically faster.</li>
</ul>
<h2><strong>Who you are</strong></h2>
<p>You are a hands-on technical leader who has built ML systems that matter, shipped them to production, and led others in doing the same. You thrive in ambiguity. You can take an open-ended problem with messy data and incomplete labels, find the highest-leverage path, get to user impact quickly, and change course when the data says so. You make the engineers around you better. You're as comfortable talking with a customer about the economics of their abuse problem as you are debugging a feature pipeline. You're excited to use frontier AI both in the systems you build and in how you build them.</p>
<p>We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.</p>
<h3><strong>Minimum requirements</strong></h3>
<ul>
<li>8+ years of experience developing, evaluating, and deploying machine learning models in a production environment.</li>
<li>A track record of technically leading large, ambiguous ML initiatives from problem definition to production, with measurable business impact.</li>
<li>Deep expertise in machine learning, statistics, model evaluation, and experiment design, including where labels are noisy, delayed, or scarce.</li>
<li>Strong software-engineering skills, and you remain a hands-on contributor who regularly writes tested, maintainable production code.</li>
<li>Experience designing and operating production ML systems at scale, including data and feature pipelines, real-time inference, deployment, and monitoring, and making architecture tradeoffs across them.</li>
<li>Experience setting technical direction across multiple engineers or teams, and mentoring engineers to grow their scope.</li>
<li>Ability to explain complex tradeoffs clearly to engineers, product leaders, and customers, and to build alignment across functions.</li>
</ul>
<h3><strong>Preferred qualifications</strong></h3>
<ul>
<li>Experience applying ML to fraud, abuse, integrity, trust and safety, identity, or security, where adversaries actively adapt to your defenses.</li>
<li>Experience with entity resolution, graph learning, or behavioral and sequence modeling of users, accounts, or devices (for example, detecting multi-accounting, account takeover, or bots).</li>
<li>Experience productionizing modern deep learning or LLM-based approaches, including using LLMs for labeling, investigation, or detection.</li>
<li>Experience building 0→1 ML products and working directly with customers to define what to build.</li>
<li>Experience building shared ML platforms, representations, or foundation models used across multiple products or teams.</li>
<li>Experience building AI-assisted tooling or agents that measurably increased a team's velocity.</li>
</ul>

Staff Machine Learning Engineer, Radar

<h2><strong>Who We Are</strong></h2>
<h3><strong>About Stripe</strong></h3>
<p>Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.</p>
<h3><strong>About The Team</strong></h3>
<p>The Solutions Architecture (SA) organization helps Stripe's most strategic customers design and validate technical solutions that drive their business forward. Within SA, our team builds the tools, workflows, and custom technical assets that make the broader SA org more effective—turning individual ingenuity into org-wide capability.</p>
<p>We are looking for AI Engineers who are energized by working close to the business and the users we serve. You'll operate as an embedded, high-context engineer focused on the highest-leverage opportunities across the SA org—building production-quality tooling, supporting our most strategic engagements, and shipping automation that meaningfully improves how Solutions Architects work every day.</p>
<h2><strong>What You'll Do</strong></h2>
<p>As an AI Engineer, you'll be embedded directly with the Solutions Architecture team—building alongside them, deeply understanding their workflows, and shipping tools and automation that permanently change how they operate. Your measure of success is SA productivity: the engagements you've accelerated, the workflows you've transformed, and the tools you've built that the org adopts as default.</p>
<p>You'll ship code daily. You'll discover where SAs lose time and build high-impact solutions. You'll take what works for one person and scale it to work for the org. And when our most strategic customer engagements need custom technical assets, you'll build those too.</p>
<p>This is a role for someone who wants to work at the intersection of engineering and business impact—close to customers, close to revenue, and building for people you can see using your work every day.</p>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Collaborate with Solutions Architects, SA leadership, Product, and Engineering to scope technical work and translate ambiguous business needs into well-defined deliverables</li>
<li>Evaluate and integrate AI capabilities (LLMs, agents, workflow automation) where they provide genuine leverage—not for novelty, but for measurable productivity improvement</li>
<li>Architect and build internal tools, agents, and automated workflows that accelerate SA and manager productivity across technical discovery, solution design, demoing, user engagements, territory/pipeline management, and product interlock</li>
<li>Take high-potential tools and workflows built by SAs and managers and harden them into scalable, maintainable, production-grade solutions</li>
<li>Identify patterns across SA workflows and proactively build solutions that address recurring friction</li>
<li>Build custom demo environments, PoC applications, and technical assets for Stripe's most strategic customer engagements</li>
<li>Document tools, architectures, and usage patterns so others can adopt and extend what you've built</li>
<li>Debug, extend, and maintain backend systems across a variety of codebases and infrastructure</li>
</ul>
<h3><strong>Minimum Requirements</strong></h3>
<ul>
<li>4+ years of experience as an engineer shipping production systems</li>
<li>Strong backend engineering fundamentals: you can debug a failing system, trace issues across services, and reason about data flows</li>
<li>Experience building and deploying AI agents, LLM-powered tools, or workflow automation beyond basic prompt engineering</li>
<li>Experience scoping and delivering work with minimal oversight in a fast-moving, cross-functional environment</li>
<li>Proficiency in at least two of: Ruby, Node.js, Python, or Next.js</li>
<li>Familiarity with cloud infrastructure (AWS, GCP) including deployment, monitoring, and basic DevOps</li>
<li>Experience building internal tools, developer platforms, or workflow automation</li>
<li>Demonstrated ability to work across multiple codebases and technology stacks simultaneously</li>
<li>Hands-on experience using AI/LLM tools in your engineering workflow—you're fluent with AI-assisted development but not dependent on it; you can reason through problems and debug without AI as a crutch</li>
<li>Strong written and verbal communication skills; you can translate technical decisions for non-technical stakeholders and navigate cross-functional collaboration naturally</li>
<li>Comfort with ambiguity—you can take a loosely-defined business problem, scope the engineering work, and ship iteratively without waiting for a perfect spec</li>
</ul>
<h3><strong>Preferred Qualifications</strong></h3>
<ul>
<li>Experience designing systems that non-engineers can build on top of or extend themselves (e.g., platforms, low-code frameworks, template systems)</li>
<li>Experience in a Solutions Engineering, Sales Engineering, or GTM Engineering role—or a product engineering role where you worked closely with customers or go-to-market teams</li>
<li>Familiarity with Stripe's products, APIs, or the payments/fintech domain</li>
<li>Experience integrating with third-party platforms (Salesforce, Gong, etc.)</li>
<li>Track record of building tools or systems that were adopted beyond your immediate team</li>
<li>Background in consulting, professional services, or other roles that blend technical depth with business context</li>
</ul>
<h2><strong>Who You Are</strong></h2>
<p>Beyond the technical requirements, we're looking for a specific kind of engineer:</p>
<ul>
<li><strong>You want to be close to the business.</strong> You're energized by seeing your work directly impact how a sales team wins a deal or how a customer succeeds.</li>
<li><strong>You're a pragmatic builder.</strong> You ship working solutions quickly, iterate based on real usage, and know when "good enough now" beats "perfect later." You'd rather show a working prototype today than present a roadmap deck next quarter.</li>
<li><strong>You're a software engineer by practice.</strong> You can architect systems, debug production issues, write clean code, and reason about tradeoffs. AI is a tool in your belt, not a substitute for engineering judgment.</li>
<li><strong>You're a pattern recognizer.</strong> When you build something that works for one person, you immediately see how it generalizes. You think in reusable systems, not one-off scripts.</li>
<li><strong>You thrive without a traditional product team structure.</strong> No PRDs landing in your lap, no dedicated PM, no sprint ceremonies. You identify the highest-leverage problem, scope the work, and ship it. You're comfortable trading on-call rotations and rigid processes for autonomy and impact.</li>
<li><strong>You're a strong communicator.</strong> You can partner with SAs who are domain experts, understand their workflows deeply enough to build great tools, and explain your technical choices to leadership.</li>
</ul>
<p> </p>

AI Engineer

<h2>Who we are</h2>
<h3>About Stripe</h3>
<p>Stripe is a technology company focused on improving the conditions for economic growth and prosperity. We build programmable financial infrastructure, rethinking from first principles how financial services should work, to make it easier and cheaper for any business to start and scale. More than 10 million <a href="https://stripe.com/customers" rel="noopener noreferrer ugc" target="_blank">businesses</a> build on Stripe, spanning the economic frontier—from solo founders to established enterprises—united by a practical focus on growth. The most ambitious companies in the world use Stripe as core infrastructure to grow faster. They process trillions of dollars a year on Stripe, equivalent to around 1.6% of global GDP. While economic growth makes everyone better off, open markets also enable greater variety. When any business can easily serve a global customer base, the quality and diversity of products in the world increase, and craft and creativity are unleashed into the smallest niches. Our own growth is wholly contingent on the success of the businesses building on Stripe. We therefore invest back into our technology at an unusual rate. We make <a href="https://stripe.com/roadmap" rel="noopener noreferrer ugc" target="_blank">upgrades to our products</a> every single day to deliver compounding gains to our customers. We maintain some of the most <a href="https://status.stripe.com/" rel="noopener noreferrer ugc" target="_blank">reliable APIs</a> on the internet. We build entirely new <a href="https://tempo.xyz/" rel="noopener noreferrer ugc" target="_blank">pieces</a> of <a href="https://mpp.dev/" rel="noopener noreferrer ugc" target="_blank">financial</a> <a href="https://joinopenstandard.com/" rel="noopener noreferrer ugc" target="_blank">infrastructure</a> to enable new ideas. And our significant advances in <a href="https://stripe.com/radar" rel="noopener noreferrer ugc" target="_blank">risk and fraud</a> infrastructure over many years are making the internet economy safer and more accessible.</p>
<p>Though people at Stripe don’t tend to take themselves seriously, Stripe is a fairly serious place: our customers are depending on us for their livelihoods. We admire ambition, intensity, curiosity, humility, and rigor. The most effective people become knowledgeable about many domains besides their own. Any company is an applied exercise in understanding some aspect of society or the market. In working with so many (especially the new and innovative ones), we think that Stripe is one of the very best places to learn about how the world works.</p>
<h3> </h3>
<h3>About the team</h3>
<p>Data Science at Stripe is a vibrant community where data analysts and data scientists learn and grow together. You'll work with some of the most fundamental data at Stripe, and use that data to help drive company-wide initiatives. We have a variety of Data Analytics roles and teams across Stripe and will seek to align you to the most relevant team based on your background.</p>
<h2> </h2>
<h2>What you’ll do</h2>
<p>As an intern at Stripe you’ll work on projects across our stack that directly impact the way millions of businesses operate. You’ll own problems end to end with the support of your manager and teammates. It’s an opportunity to work alongside some of the most creative and technically rigorous data analysts and scientists in the industry. You'll partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business recommendations through analyses and data storytelling.</p>
<p>Our internship program is competitive and the expectations are high.</p>
<p> </p>
<h3>Responsibilities</h3>
<ul>
<li>Analyze data and running experiments to drive impact</li>
<li>Ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly</li>
<li>Create metrics, reports, and dashboards to inform and run the business</li>
<li>Extend Stripe’s metrics semantic layer</li>
<li>Build new data assets and improve existing data assets to support decision-making, products, and business processes</li>
<li>Assess the impact of decisions, product launches, and product improvements using business analytics and A/B testing</li>
<li>Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly</li>
<li>Present your work to the Data Science team, partner teams, and fellow interns</li>
</ul>
<p> </p>
<h2>Who you are</h2>
<ul>
<li>Ambitious builder: You’re energized by building solutions without clear precedent and solving problems with far-reaching consequences. Successful Stripes are deeply curious, and prefer the joy of discovery to the comfort of certainty.</li>
<li>Rigorous thinker: You appreciate that things worth doing are rarely simple. You enjoy working on problems that have never been tackled before.</li>
<li>Adaptable problem solver: You adapt quickly and treat obstacles as opportunities. At Stripe we embrace kindness while encouraging Stripes to take measured risks and act boldly, even in the absence of consensus.</li>
</ul>
<h3> </h3>
<h3>Minimum requirements</h3>
<ul>
<li>Enrolled in a quantitative <strong><em>Bachelor’s or Master's degree program</em></strong> (e.g. Data Analytics, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in December 2027 or spring/summer 2028</li>
<li>Experience with SQL</li>
<li>Proficiency with AI tools to accelerate model development, analysis, and coding</li>
<li>Experience communicating and collaborating with multidisciplinary stakeholders in a team environment</li>
</ul>
<h3> </h3>
<h3>Preferred qualifications</h3>
<ul>
<li>Experience writing and debugging data pipelines</li>
<li>Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects</li>
<li>Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts</li>
</ul>

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Credit Risk, Fraud Strategist

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