CO - 高级数据科学家 - 229

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).

Originally posted on Himalayas