OpenX

• Design large-scale data processing systems. • Work with Product to drive the requirements, and own the project end-to-end. • Analyze and improve the efficiency, scalability, and stability of applications. • Think long-term and be unsatisfied with band-aids. • Identify unnecessary complexity and remove it.

• Design, write and deliver software to implement and support large web-scale, highly-performant, highly-available infrastructure on GCP/AWS (e.g. Terraform) • Monitor infrastructure, respond to incidents, correct and improve systems to prevent incidents, and plan capacity • Support system deployments and product releases • Tune large-scale clusters for optimal performance and efficiency • Work closely with engineering, project management, and operational peers to develop innovative technical tools and solutions • Participation in on-call rotation

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining. • Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems. • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate. • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance. • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance. • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems. • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions. • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes. • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration. • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation. • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills. • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise. • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews). • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences. • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining. • Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems. • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate. • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance. • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance. • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems. • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions. • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes. • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration. • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation. • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills. • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise. • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews). • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences. • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

• Translate the complete website UI/UX based on the design from Figma/Sketch into pixel-perfect, functional, and responsive web pages • Manage a responsive website for both desktop & mobile • Implement & manage cookie banners across all web pages • Bring in new web development practices & learning to deliver a great customer experience • AI Accessibility - Experience with or interest in leveraging AI tools to improve development efficiency (e.g., GitHub Copilot, Cursor) and enhance user experience (e.g., AI-powered search, content personalization, translation). • Enhance our website with pages including menu, footer, and sitemap to be managed and updated by the marketing manager for copy and assets • Manage code injection, analytics, cookies, asset library, etc. • Take care of any WordPress CMS needs as needed, including upgrades to WordPress, plugins, and custom themes. This includes rigorous testing on a staging environment to prevent production issues. • Create and maintain clear technical documentation for the website's codebase, architecture, and deployment processes. • Manage our WP Engine hosting • Own the website's overall security posture by implementing preventative measures (like vulnerability scanning), monitoring for emerging threats, and defending against malicious traffic and potential exploits • Manage and verify database backups • Set up and manage an architecture of a staging environment to version manage the website • Website Performance Optimization - Continuous optimization of front-end performance to meet Google Core Web Vitals, ensuring fast and smooth performance on all devices

• Design and maintain reliable automated tests across TypeScript-based UI and Python API layers. • Own and evolve test automation for complex frontend user workflows. • Build, maintain, and improve automated UI test suites using Playwright and TypeScript. • Contribute to API integration and end-to-end testing strategy, automation, and coverage using Python and pytest. • Be involved in all phases of product development, from requirements and solution design to validation, deployment, and release readiness. • Partner with software engineers, product managers, and UX designers to identify quality risks early and improve testability. • Apply risk-based testing practices and make pragmatic decisions about test coverage at unit, integration, API, and end-to-end levels. • Improve the reliability, speed, and maintainability of test automation frameworks and CI/CD pipelines. • Investigate test failures, flaky tests, and production issues, and drive root cause analysis with actionable improvements. • Communicate quality risks, trade-offs, and recommendations clearly to engineering and product stakeholders.

• Own and scale performance and revenue reporting tools for internal customers to drive data-informed decisions • Lead automation and optimization of Finance data pipelines and reporting processes • Deliver impactful ad-hoc analysis and reporting to support teams across the company • Develop, lead, and execute deep dive quantitative analyses that translates data into actionable insights • Provide analytical and decision-making support for key company initiatives • Present analysis and share findings with both technical and non-technical stakeholders

• Test large-scale event processing systems and back-end applications • Involved in all phases of product development, starting from requirements to validation and deployment • Analyze and improve efficiency, scalability, and stability of applications • Work in a focused and nimble team that collaborates on common problems across products and across focus areas • Own components, processes, and workflows that demand professionalism, excellent communication, teamwork, and documentation skills • Create, maintain, and improve automation test suites/frameworks • Monitor and maintain QA CI pipelines, collaborate on CD enablement • Conduct root cause analysis of issues and provide solutions for better coverage and maintainability • Effectively communicate and be a crucial member of both the project teams and QA organization • Be exposed to a broader set of problems, allowing for fast career growth and proximity to abundant technical problems

• Design and build high-quality, maintainable user interfaces using JavaScript/TypeScript and React. • Develop and maintain Python-based backend APIs that support frontend workflows and product capabilities. • Translate business rules, pricing logic, and data dependencies into intuitive user experiences and reliable API integrations. • Own features end-to-end, from requirements and technical design through implementation, testing, rollout, and operational quality. • Write and maintain automated tests as part of the development process, including frontend and API-level tests where appropriate. • Collaborate closely with Product, UX, QA, and Engineering to deliver reliable product experiences for enterprise users. • Improve the usability, reliability, and maintainability of existing systems. • Contribute to technical direction, code quality, and engineering practices within the team.

• Architect robust, scalable, and maintainable ML systems for broad use across the exchange, spanning training pipelines, real-time inference, validation, and monitoring • Serve as a go-to expert in deep learning and related advanced data science domains, with the breadth to guide method selection across adjacent areas • Lead the evaluation and adoption of new AI/ML modeling techniques, frameworks, and research advancements relevant to OpenX’s marketplace problems • Define and evolve technical standards and best practices within their domain, influencing broader adoption across the organization • Identify and lead high-impact, cross-team data science initiatives, such as improving bidding strategies, building new prediction systems, or redesigning experimentation frameworks • In partnership with engineering and product leadership, drive the data science roadmap for a product area or platform capability • Partner with product managers and commercial stakeholders to translate marketplace problems into data science solutions with measurable business outcomes • Solve novel, ambiguous problems requiring innovation in methodology, algorithms, or feature engineering • Mentor and develop senior data scientists, helping them grow toward broader technical leadership • Raise the skills, impact, and scientific rigor of teams around them through guidance, architectural patterns, and strategic direction • Communicate technical strategy and build consensus for complex decisions across senior leadership and cross-functional teams