Senior Data Scientist

As a Senior Data Scientist at Kard, you will build and deploy machine learning and experimentation systems that power our card-linked offers platform. Your work will directly improve how users discover and engage with offers, and how partners measure ROI. You’ll operate across personalization, ranking, and causal measurement - partnering closely with Product, Engineering, and Sales to turn behavioral transaction data into production-grade models and insights.

• 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.

• 6+ years of experience in data science, with meaningful experience in applied machine learning in production.
• Strong experience with recommendation systems, ranking models, or personalization (e.g., propensity models, collaborative filtering, embeddings).
• Solid grounding in statistics and experimentation, including A/B testing and incrementality measurement.
• Experience with causal inference approaches for real-world observational data.
• Proficiency in Python (pandas, scikit-learn, PyTorch/XGBoost) and SQL; experience working with large-scale datasets.
• Experience working with event-driven or transaction-level data (fintech, ads, marketplaces, or similar domains preferred).
• Familiarity with modern data/ML stacks (e.g., Spark, Airflow, dbt, feature stores, cloud platforms like AWS/GCP).