Senior Data Scientist
K
KardFintech, Commerce Media
Remote, U.S. core business hoursFull-TimeSenior
Salary160,000 - 200,000 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLMachine LearningPyTorchSparkA/B testingdbt
Requirements
- 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, specifically event-driven or transaction-level data.
- Familiarity with modern data/ML stacks (e.g., Spark, Airflow, dbt, feature stores, cloud platforms like AWS/GCP).
- Experience collaborating with engineers to deploy models into production systems (APIs, batch jobs, real-time scoring).
- Ability to connect modeling work to business outcomes like conversion, lift, retention, and ROI.
- Willingness to travel for company meetings.
Responsibilities
- 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.
- Design and analyze A/B tests and incrementality experiments to measure campaign and model impact.
- Apply causal inference methods 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.
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