Senior/Staff Data Scientist, Consumer Apps - Klover

About the role

Hands-on development of ML and statistical models at the core of our EWA product, with a focus on fast, rigorous, and high-quality execution.
Build and improve predictive models across consumer decisioning, consumer behavior modeling, fraud, churn, transaction intelligence, and other business-critical use cases.
Own the full model development lifecycle, including data exploration, feature engineering, model training, validation, deployment, monitoring, and retraining.
Develop reusable modeling pipelines, analytical tools, and production-quality code to support scalable data science work.
Apply strong statistical and mathematical judgment to model evaluation, calibration, robustness testing, and business impact measurement.
Collaborate with data analysts, engineers, product managers, and business stakeholders to deliver ML models with quality, efficiency, and precision.
Identify new areas where data science, predictive modeling, and optimization can improve product and business outcomes.

Preferred Qualifications

• 5+ years of direct experience working as a Data Scientist, Machine Learning Scientist, Model Developer, Applied Scientist, Economist, or similar role on relevant business problems
• Strongly preferred: Master's, or Ph.D. in a STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field
• Demonstrated ability to apply critical thinking, causal inference, abstract reasoning, and generalization to complex, ambiguous business and technical problems
• Strong expertise developing, validating, deploying, and monitoring machine learning models in production
• Experience with AI/ML-assisted development tools and MLOps practices, including experience working with large language models (LLMs) or autonomous agents for code generation and model refinement
• Experience with predictive modeling, consumer behavior modeling, risk modeling, credit decisioning, fraud modeling, churn modeling, or other high-impact applied ML use cases
• Solid foundation in statistics, probability, mathematics, and machine learning fundamentals
• Strong Python coding skills, with the ability to build models, pipelines, and analytical tools from scratch
• Strong SQL skills and experience working with large, messy, real-world datasets
• Experience with feature engineering, model evaluation, calibration, monitoring, retraining, and model performance diagnostics
• Experience with cloud computing services or platforms; GCP preferred
• Familiarity with version control, peer code review, and collaborative software development practices
• Demonstrated ability to learn new technologies, applications, and modeling approaches quickly
• Willingness to roll up your sleeves and wear multiple hats across data science, analytics, modeling, and technical execution based on business needs
• Strong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences