Role Description
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. Youβll 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.
Responsibilities
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Build and ship ML models that drive offer personalization, ranking, and targeting using transaction, merchant, and user behavioral data.
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Develop features and training pipelines on top of large-scale event and transaction datasets (e.g., spend patterns, visit frequency, merchant affinity).
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Design and analyze A/B tests and incrementality experiments to measure campaign and model impact.
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Apply causal inference methods (e.g., matching, uplift modeling, diff-in-diff) to quantify partner ROI and user behavior changes.
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Partner with ML and Data Engineers to productionize models, including feature stores, batch/real-time scoring, and monitoring.
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Improve and iterate on ranking/recommendation systems to optimize engagement, conversion, and retention.
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Contribute to attribution and measurement systems that help brands understand incremental value from Kard campaigns.
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Translate complex modeling outputs into clear, actionable insights for internal teams and external partners.
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Own projects end-to-end: problem framing, data exploration, modeling, deployment, and post-launch evaluation.
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Help define best practices for experimentation, model evaluation, and data quality across the team.
Qualifications
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6+ years of experience in data science, with meaningful experience in applied machine learning in production.
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Strong experience with recommendation systems, ranking models, or personalization (e.g., propensity models, collaborative filtering, embeddings).
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Solid grounding in statistics and experimentation, including A/B testing and incrementality measurement.
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Experience with causal inference approaches for real-world observational data.
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Proficiency in Python (pandas, scikit-learn, PyTorch/XGBoost) and SQL; experience working with large-scale datasets.
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Experience working with event-driven or transaction-level data (fintech, ads, marketplaces, or similar domains preferred).
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Familiarity with modern data/ML stacks (e.g., Spark, Airflow, dbt, feature stores, cloud platforms like AWS/GCP).
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Experience collaborating with engineers to deploy models into production systems (APIs, batch jobs, real-time scoring).
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Ability to connect modeling work to business outcomes like conversion, lift, retention, and ROI.
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Strong communication skills; able to explain tradeoffs and results to both technical and non-technical audiences.
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Pragmatic, product-minded, and impact-driven.
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U.S. core business hours availability and willingness to travel for company meetings.
Benefits
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Flexible PTO, with a minimum requirement to take one day off a quarter, ten days a year, promoting mental well-being.
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Observance of 11 Federal Holidays, plus an additional day each for Black Friday and Christmas Eve (US employees only).
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Health, Dental and Vision Insurance.
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401k with employer match (US employees only).
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Coworking space and WFH setup reimbursement.
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Company Offsites two times per year for planning, team-building, and networking.