Stripe

<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>Our Data Science team partners 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. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. </p>
<p>The Link Data Science team at Stripe is the dedicated data science and analytics partner for Link - currently with a strong product market fit in offering one-click checkout to shoppers who shop across Stripe’s merchant network, trusted by more than 300M users. The Link team has an exciting roadmap to launch consumer-friendly features to make Link the best way to spend, while continuously generating conversion uplift to our merchants. We are hiring for two data scientists dedicated to:</p>
<ol>
<li><span>Local Payment Methods </span>- We want to enable consumers across the globe to be able to pay using their preferred local payment method like UPI, PIX etc. This allows merchants to get conversion uplift from reduced friction as consumers get to pay with the payment method that is most accessible for them. In this role, you get to drive the enablement and support of more local payment methods, run analyses to surface friction points and improve the product, and be a pioneer in shaping consumer payment method preferences. </li>
<li><span>Link Consumer Team</span> - Beyond the one-click accelerated checkout product, the Link team has also shipped a lot of value-added features for our consumers. On the Link App you can review your subscriptions, add more than one payment methods so you can choose the right payment method that maximizes your rewards on checkout without having to manually fill it up anywhere, and  even an agentic AI-wallet that allows your preferred AI model to transact on your behalf without exposing your payment credentials. </li>
</ol>
<p><br /><strong>What you'll do</strong></p>
<p>You'll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics.</p>
<h2><strong>Who you are</strong></h2>
<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>PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience</li>
<li>3+ years in Product Analytics, Experimentation and Causal Inference</li>
<li>Experience designing, running, and analyzing complex experiments or leveraging causal inference designs</li>
<li>Proficiency in SQL and Python</li>
<li>Experience in working with cross-functional teams to deliver results</li>
<li>Ability to communicate results clearly and a focus on driving impact</li>
<li>A demonstrated ability to manage and deliver on multiple projects with a high attention to detail</li>
<li>Strong business acumen and experience in synthesizing complex analyses into actionable recommendations</li>
<li>Proficiency with AI tools to accelerate model development, analysis, and coding</li>
</ul>
<h3><strong>Preferred qualifications</strong></h3>
<ul>
<li>Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation</li>
<li>Experience deploying models in production and adjusting model thresholds to improve performance</li>
<li>A builder's mindset with a willingness to question assumptions and conventional wisdom</li>
<li>Experience with distributed tools such as Spark, Hadoop, etc.</li>
<li>A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)</li>
</ul>
<h2><br /><br /></h2>

Data Scientist, Link

<h2>Who we are</h2>
<h3>About Stripe</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>About the team</h3>
<p>Our Data Science team partners 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. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust.</p>
<h2>What you'll do</h2>
<p>We're looking for a Data Scientist to join the Fraud Data Science team. In this role, you'll build and improve the models that power Stripe's fraud detection and loss management systems. You'll work closely with Fraud Engineering and Risk Operations to move models from research to production, and you'll use data to surface insights that shape fraud strategy across the business.</p>
<p>Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk problems in global payments.</p>
<h2>Who you are</h2>
<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>Minimum requirements</h3>
<ul>
<li>PhD with 1-3 years, MS or MA with 2-6 years, or BS or BA with 4-8 years of data science or quantitative modeling experience</li>
<li>Experience with Fraud, Risk or Financial Crimes</li>
<li>Proficiency in SQL and a computing language such as Python or R</li>
<li>Experience in working with cross-functional teams to deliver results</li>
<li>Ability to communicate results clearly and a focus on driving impact</li>
<li>A demonstrated ability to manage and deliver on multiple projects with a high attention to detail</li>
<li>Strong business acumen and experience in synthesizing complex analyses into actionable recommendations</li>
<li>Proficiency with AI tools to accelerate model development, analysis, and coding</li>
</ul>
<h3>Preferred qualifications</h3>
<ul>
<li>Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, causal inference, and experimentation</li>
<li>Experience deploying models in production and adjusting model thresholds to improve performance</li>
<li>Experience designing, running, and analyzing complex experiments or leveraging causal inference designs</li>
<li>A builder's mindset with a willingness to question assumptions and conventional wisdom</li>
<li>Experience with distributed tools such as Spark, Hadoop, etc.</li>
<li>A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)</li>
</ul>

Data Scientist, Fraud

<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>Build statistical models, define and analyze product and operational metrics, explore experimental design, and construct exploratory analysis with internal data. </li>
<li>Work closely with product and business teams to identify important questions and answer them with data. </li>
<li>Collaborate with other data scientists, engineers and operations to formulate innovative solutions to experiment and implement advanced data mining techniques. </li>
<li>Conduct exploratory analysis on internal data to understand user behavior to inform product development. </li>
<li>Drive the collection of new data and the refinement of existing data sources. </li>
<li>Apply statistical and machine learning models on large datasets to measure results and outcomes, and identify causal impact and attribution. </li>
<li>Predict future performance of users or products. </li>
<li>Define, measure, and monitor key outcome metrics for teams and support Stripe’s business. </li>
<li>Communicate complex concepts and the results of metrics and analyses in a clear and effective manner through creative visualization. </li>
<li>Communicate findings broadly and interact with other teams including product managers, software engineers, marketing, and business development.</li>
</ul>
<h2><strong>Who you are</strong></h2>
<h3><strong>Minimum requirements</strong></h3>
<p>Must have a Master's degree or foreign equivalent in Operations Research, Statistics, Industrial Engineering, Business Analytics, Mathematics or a related field, plus three (3) years of experience in Data Science or a related occupation.</p>
<p>Must have three (3) years in each of the following:</p>
<ul>
<li>Statistical and machine learning methods;</li>
<li>Numerical optimization &amp; integer programming;</li>
<li>SQL;</li>
<li>Python;</li>
<li>Optimization; and</li>
<li>Forecasting and machine learning domain.</li>
</ul>
<p>Salary: $192,000.00 - $288,000.00/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>50% Telecommuting Permitted.</p>
<p>Up to 10% domestic travel required.</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>WA34</strong></p>
<h3><strong> #LI-DNI</strong></h3>

Senior Data Scientist

<h2>Who we are</h2>
<h3>About Stripe</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>About the team</h3>
<p>Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure.</p>
<h2>What you’ll do</h2>
<p>You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company.</p>
<h3>Responsibilities<span> </span></h3>
<ul>
<li>Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. </li>
<li>Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. </li>
<li>Working directly with product teams and ML engineers to improve their day-to-day productivity. </li>
<li>Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies.</li>
</ul>
<h2><strong>Who you are</strong></h2>
<p>We’re looking for people with a strong background or interest in building successful products or systems; you’re passionate about solving business problems and making impact, you are comfortable in dealing with lots of moving pieces; and you’re comfortable learning new technologies and systems. You are comfortable working with other Stripe teams across the US and Canada.</p>
<h3>Minimum requirements</h3>
<ul>
<li>2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems</li>
<li>Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations</li>
<li>Experience working on production ML platforms, MLOps solutions, or building LLM applications</li>
<li>Experience running operations for high availability, low latency systems</li>
<li>Experience partnering with other teams to drive business outcomes</li>
<li>A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course</li>
</ul>
<h3><strong>Preferred qualifications</strong><span> </span></h3>
<ul>
<li>Experience building and shipping production AI agents</li>
<li>Familiarity with the LLMs and LLM Frameworks</li>
<li>Experience training and shipping machine learning models to production to solve critical business problems</li>
</ul>

Software Engineer, Machine Learning Infrastructure

<div>
<h2>Who we are</h2>
<h3>About Stripe</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>About the team</h3>
<p>You’ll be joining the data science team at Stripe responsible for our overall infrastructure, with a focus on core systems and cloud platforms. Projects include, but are not limited to:</p>
<ul>
<li>Developing models to predict resource needs as Stripe demand increases;</li>
<li>Working closely with engineers to improve the cost and performance of platforms and services;</li>
<li>Employing quantitative methods to drive and automate fleet decisions.</li>
</ul>
<p>You will act as a key strategic data partner to the Core Infrastructure organization at Stripe, and help craft, guide, and drive the strategy and tactics needed to help ensure Stripe can continue to scale with efficiency and dependability as our business rapidly grows.</p>
<h2>What you'll do</h2>
<p>As a Data Scientist, your role will involve:</p>
<ul>
<li>Analyzing infrastructure usage, efficiency, and workloads to predict demand and inform capacity planning.</li>
<li>Developing models and strategies for efficient compute resource consumption and provisioning.</li>
<li>Collaborating with engineers, engineering leadership, and finance teams to ensure Stripe makes the right, data-driven, infrastructure decisions.</li>
<li>Providing actionable insights and recommendations to improve infrastructure operations to reduce costs and improve reliability.</li>
<li>Utilizing your analytical expertise to influence both technical and financial strategies within Stripe.</li>
</ul>
<h2>Who you are</h2>
<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>Location Requirement</h3>
<ul>
<li>Seattle, WA or San Francisco, CA (Hybrid: 50% in office)</li>
</ul>
<h3>Minimum requirements</h3>
<ul>
<li>PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience.
<ul>
<li>3-8+ years of experience with a focus on infrastructure, cloud environments, and resource utilization/allocation.</li>
</ul>
</li>
<li>Proficiency in SQL and a computing language such as Python or R.</li>
<li>Experience in analyzing logs/telemetry, scheduling optimization, or cloud infrastructure engineering.</li>
<li>Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results.</li>
<li>A demonstrated ability to manage and deliver on multiple projects with a high attention to detail.</li>
<li>Solid business acumen and experience in synthesizing complex analyses into actionable recommendations.</li>
<li>A track record of building relationships with and influencing the decisions of senior technical leadership.</li>
<li>A builder's mindset with a willingness to question assumptions and conventional wisdom.</li>
</ul>
<h3>Preferred qualifications</h3>
<ul>
<li>Background in deploying data models in production environments and optimizing their performance.</li>
<li>Experience in using, deploying on, and analyzing usage data from public cloud providers.</li>
<li>Familiarity with distributed computing tools such as Spark and Hadoop.</li>
<li>A PhD or MS in a quantitative field like Computer Science &amp; Engineering, Statistics, Mathematics, Operations Research, Industrial Engineering, Management Science, or related disciplines.</li>
<li>Strong business acumen with a track record of translating complex data analyses into actionable business recommendations.</li>
</ul>
</div>

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