Staff Data Scientist

Kard is transforming customer loyalty with our rewards-as-a-service API platform. We empower our partners, from neobanks to financial institutions and beyond, to offer tailored rewards that celebrate customers’ daily transactions. Backed by $30M from industry-leading investors and seasoned experts, we are reshaping the future of customer engagement.

About the Role

As a Staff Data Scientist  at Kard, you’ll lead development of advanced personalization, optimization, and measurement systems at the heart of our platform. You will apply deep expertise in statistical modeling, experimentation, and machine learning to improve loyalty campaign performance and user experience. This highly impactful, cross-functional role spans experimentation frameworks, causal inference, and applied ML—contributing to both platform capabilities and strategic decisions.

Responsibilities

• Design and lead advanced personalization and optimization models to improve campaign targeting, offer performance, and user outcomes.
• Build and scale experimentation frameworks for A/B tests, lift studies, and causal inference pipelines.
• Develop attribution and measurement methodologies to help partners understand program impact.
• Translate statistical and modeling results into actionable, business-relevant insights for Product, Engineering, Sales, and partners.
• Collaborate with ML Engineers to operationalize models in production environments.
• Mentor other data professionals and provide thought leadership on statistical best practices and model design.
• Drive complex, high-impact projects from ideation to delivery, influencing product and platform strategy.
• Advance Kard’s measurement and experimentation infrastructure to deliver robust, reproducible insights.

Desired Skills

• Experience working in data science, focused on experimentation, personalization, or performance measurement.
• Significant experience with statistical hypothesis testing and causal inference on large datasets—ideally in digital ads or similar performance-driven domains (fintech, healthcare, economics, finance, B2B SaaS).
• Expertise in both frequentist and Bayesian methods, with strong theoretical foundation and ability to prototype real-world solutions.
• Proficiency in machine learning techniques (scikit-learn, XGBoost, PyTorch, etc.) for both inference and model development.
• Strong SQL and Python skills, with experience working at scale on complex datasets.
• Proven track record of translating statistical and ML insights into business or product impact.
• Experience collaborating with product and engineering teams to build data-driven features; production experience is a plus.
• Excellent communication skills and ability to simplify complex topics for diverse audiences.
• Experience mentoring and guiding data science peers.
• Bachelor’s or Master’s in a quantitative field (Statistics, Computer Science, Mathematics, Economics); Ph.D. a plus but not required.
• Inclusive, collaborative, humble, and impact-oriented mindset.
• U.S. core business hours availability and willingness to travel for company meetings.