Data Scientist - PBM Pricing Optimization and Advanced Analytics

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Senior Data Scientist - Network Value (Credit) The Network Value Data Science team is helping Plaid build an industry-leading fintech consumer network with best-in-class products and user experiences. We are a product analytics team embedded in key product areas across Plaid. We support some of Plaid’s most important OKRs and help execute on product roadmaps. We translate ambiguous product questions into tractable analysis, serve as analytical thought partners throughout the org, identify opportunities to build better products, and champion a data-first decision-making approach everywhere we go. You’ll be a Data Scientist supporting Credit, a critical product area within Plaid’s Network Value portfolio. You’ll become a data and analytical thought partner to product managers, engineers, and cross-functional stakeholders, helping shape Credit product strategy, improve product performance and user experience, and grow Plaid’s consumer network. You’ll translate business questions into analytics projects, perform ad-hoc and strategic analysis, improve visibility into core systems through data modeling and dashboarding, create OKRs and KPIs tied to business goals and user experiences, a

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We’re hiring a Data Scientist to help build and scale our internal analytics capabilities. This is a foundational role where you’ll create dashboards, data models and insights to power business and product teams alike. You’ll collect requirements, define key metrics, and deliver insights directly to stakeholders. You'll define what success looks like across a technical, usage-based platform and turn ambiguous questions into analyses, forecasts, and experiments that shape Baseten’s product and strategy. RESPONSIBILITIES Build and maintain production-grade dbt models and dashboards across multiple functions with a focus on accuracy, simplicity and user experience. Define and instrument core metrics around ROI, product adoption, customer lifecycle, capacity, availability, revenue and costs. Ingest and transform raw data using tools like dbt, Airbyte, and BigQuery. Partner with Engineering, Finance, Marketing, and Sales teams to understand goals and translate them into data solutions REQUIREMENTS 5+ years of experience in analytics engineering, data analysis, analytics, data science or a related role Advanced SQL and dbt skills, with a record of building models, tests, semantic layers and lineage in a cloud data warehouse. Prior experience supporting complex cross-functional projects across GTM, Finance and Engineering across various stages of the customer journey. Experience building dashboards and self-serve analy

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Data Scientist What is the opportunity? We are seeking a highly skilled and motivated Data Scientist to join our Cyber Analytics team within the Security Solutions Data Science organization. This role is critical to driving advanced analytics initiatives, improving fraud detection capabilities, and supporting strategic decision-making across cybersecurity and payment fraud domains. What will you do? • Gain subject matter knowledge on web application security, commonly exploited cyber vulnerabilities, and methods of online and payment card fraud including the common points of purchase for compromised cards. • Build, develop, and maintain innovative data-driven analytical solutions, including predictive models and machine learning algorithms, on large volumes of data to support analytics and reporting needs across products, markets, and services. • Competently handle large datasets, sifting for patterns and trends and translating those insights into technical rules and solutions. • Combine cybersecurity and transaction data into new and insightful views of fraud and vulnerability across the Mastercard network. • Collaborate with cross-functional teams including product, engineering, and operations to understand product, usage, and data pipelines as well as delivering scalable solutions. • T

About the Role OpenAI’s Industrial Compute organization is responsible for ensuring our compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models. We’re looking for a Data Scientist to partner closely with Capacity Systems Engineering, Infrastructure, Product, and Research to optimize inference capacity across our global GPU fleet. This role combines statistical modeling, large-scale data analysis, forecasting, and systems thinking to drive critical decisions around infrastructure investments, performance-efficiency trade-offs, and customer experience. You’ll transform complex operational data into actionable insights that directly influence how OpenAI allocates and scales one of the world’s largest AI compute environments. Key Responsibilities Build statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and overall fleet efficiency. Develop forecasting models for inference demand across products, regions, and model families. Analyze production workloads to identify latency bottlenecks and capacity constraints, highlighting optimization opportunities. Partner with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies. Design experiments and simulations to evaluate scheduling policies, serving strategies, and infrastructure tradeoffs. Build dashboards and operational metrics that enable leadership to make data-driven capacity decisions. Collaborate with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps. Communicate technical findings clearly to both engineering teams and executive leadership. Qualifications MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or related quantitative discipline (or equivalent industry experience). 5+ years of experience working in the infrastructure data science space. Strong ex

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Data team within Plaid’s Fraud organization. We build the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s network data to help identify and prevent fraud before it happens. Our team owns the end-to-end ML lifecycle, from feature pipelines and model training to production serving and monitoring, ensuring our systems are reliable, scalable, and built to support hundreds of customers and data partners. As a Data Scientist on the Fraud Data team, you will analyze customer and network traffic to understand how Plaid Protect performs across a range of use cases and customer segments. You’ll build dashboards and metrics that provide a clear, shared view of product performance, run backtests to evaluate performance and identify high-impact rules and model strategies, and generate insights that support customer growth and expansion. You’ll also design scalable data models and schemas to enable reliable analysis and reporting, while partnering closely with Product and Engineering to design and analyze experiments for new customer-facing features. Responsibilities: Work at the intersection of product analytics, machine learning, and fraud a

About the Team The Statsig team at OpenAI builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the Role We are hiring a Staff-level Data Scientist to help lead the evolution of OpenAI’s core experimentation platform. This role is focused on improving the statistical rigor, reliability, and practical usability of experimentation across the company. You’ll work on some of the hardest problems in online experimentation: sample ratio mismatch detection, variance reduction, bias mitigation, metric design, triggered analysis, heterogeneous treatment effects, sequential testing, and experimentation in complex ML systems. You’ll also help translate advanced statistical concepts into pragmatic systems and product experiences that teams can actually use. This is a highly technical individual contributor role with significant influence across methodology, platform architecture, and experimentation best practices. The ideal candidate combines deep statistical expertise with strong systems intuition and hands-on experience building or operating experimentation platforms at scale. In this role, you will: Drive the statistical direction and technical strategy for OpenAI’s experimentation platform Design and improve experimentation methodolo