Clearco is a leader in AI and data-driven eCommerce funding, providing non-dilutive capital that helps founders grow without sacrificing equity. We are hiring a Senior Data Scientist to shape the models, experiments, and analytics that drive our risk, underwriting, and revenue decisions.
This hands-on senior role sits at the intersection of Data Science, Machine Learning, and Product. You will partner with Engineering, Product, Risk, and Finance to turn ambiguous problems into production-grade models and measurable outcomes that responsibly scale funding for eCommerce businesses.
Responsibilities
- Design and execute data science experiments, including causal analysis, A/B tests, and offline evaluation.
- Develop, evaluate, and iterate on predictive models for credit/risk scoring, revenue forecasting, and policy performance.
- Own model performance and monitoring: define success metrics, investigate drift, and drive improvements to data quality and feature reliability.
- Partner with Product Engineering to productionize models and analytics with emphasis on reliability, reproducibility, and maintainability.
- Perform exploratory data analysis, feature engineering, and robust validation on real-world, messy data.
- Communicate insights and recommendations clearly to technical and non-technical stakeholders through documentation and presentations.
- Improve analytical standards, code review practices, and documentation to raise technical quality.
- Mentor and support team members through pairing, feedback, and sharing best practices.
Requirements
- 5+ years of professional experience in data science, applied machine learning, or a related quantitative role.
- Strong foundations in statistics and experimentation, including hypothesis testing, causal reasoning, and evaluation design.
- Proven experience building and shipping predictive models (classification, regression, time series) and measuring real-world impact.
- Strong proficiency in Python and SQL and comfort working with production data workflows.
- Experience defining success metrics, aligning with stakeholders, and delivering end-to-end outcomes.
- Strong written communication skills and a pragmatic approach to fast-moving environments.
- Experience owning model performance, monitoring for drift, and improving feature reliability.
Nice to Have
- Experience with credit risk, underwriting, fraud/risk signals, or financial forecasting.
- Experience with modern data tooling and warehouses such as BigQuery or Snowflake and transformation frameworks like dbt.
- Familiarity with MLOps patterns (model deployment, monitoring, feature stores, orchestration) and cloud environments.
- Experience working with messy third-party data sources (banking data, eCommerce platforms, marketing signals).