Kard

• Work the full sales cycle from prospecting and qualification through negotiation and closing of net-new business opportunities within your assigned territory and verticals • Build and maintain a healthy pipeline of opportunities through a combination of outbound prospecting, inbound lead follow-up, referrals, and other sourcing strategies • Develop a strong understanding of the Kard platform and effectively communicate our value proposition to prospective clients • Conduct virtual and in-person meetings, presentations, and product demonstrations with marketing and business decision-makers at mid-market and enterprise brands • Collaborate with Sales Leadership, Account Management, Marketing, Product, Data, and Finance teams to support the sales process and deliver a strong customer experience • Maintain accurate CRM records, pipeline updates, forecasting, and reporting in accordance with company processes • Consistently achieve and exceed individual sales targets and performance metrics • Travel as needed for client meetings, industry events, and company meetings • Additional duties as assigned

• Build and ship ML models that drive offer personalization, ranking, and targeting using transaction, merchant, and user behavioral data. • Develop features and training pipelines on top of large-scale event and transaction datasets (e.g., spend patterns, visit frequency, merchant affinity). • Design and analyze A/B tests and incrementality experiments to measure campaign and model impact. • Apply causal inference methods (e.g., matching, uplift modeling, diff-in-diff) to quantify partner ROI and user behavior changes. • Partner with ML and Data Engineers to productionize models, including feature stores, batch/real-time scoring, and monitoring. • Improve and iterate on ranking/recommendation systems to optimize engagement, conversion, and retention. • Contribute to attribution and measurement systems that help brands understand incremental value from Kard campaigns. • Translate complex modeling outputs into clear, actionable insights for internal teams and external partners. • Own projects end-to-end: problem framing, data exploration, modeling, deployment, and post-launch evaluation. • Help define best practices for experimentation, model evaluation, and data quality across the team.

• Design and implement frontend systems in React and TypeScript, with attention to performance, accessibility, and maintainability. • Own the front of the frontend: design tokens, CSS architecture, and the tooling that makes styling scalable and consistent. • Establish and evolve testing practices that catch real bugs without creating brittleness—favoring tests that support refactoring over tests that fight it. • Contribute to backend services in Go, building APIs and integrations that support frontend needs. • Collaborate with product, design, and engineering to shape features from early concept through delivery, building in narrow vertical slices that deliver value continuously. • Drive frontend architecture decisions, balancing trade-offs between developer experience, optionality, performance, and business impact. • Improve automation, CI/CD, and observability for frontend systems, enabling faster iteration and more reliable releases. • Mentor engineers and establish standards—proposing them quickly, building consensus, shipping them, and evolving them thoughtfully when circumstances change.

• Build and maintain robust data models using dbt, ensuring data accuracy and accessibility across the organization through automated processes and improved data quality checks • Design and migrate dashboards and reports to modern BI tools like Count, translating complex data requirements into clear visualizations that support merchant and issuer success. • Develop analytical frameworks for merchant segmentation, performance metrics, and campaign measurement, providing insights that drive strategic decisions and optimize partner relationships. • Partner with sales, account management, and product teams to understand data needs, scope technical solutions, and deliver analytics that directly support revenue growth and operational efficiency. • Lead pitch development and technical investigations, identifying opportunities to improve data workflows, reduce costs, and enhance the scalability of analytics infrastructure.