CO - Sr. Data Scientist - 229

Thaloz; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Clearco in the United States is seeking a Senior Data Scientist to shape models, experiments, and analytics that drive risk, underwriting, and revenue decisions for our data-driven funding platform.

This hands-on role sits at the intersection of Data Science, ML, and Product. You will partner with Engineering, Risk, and Finance to turn ambiguous problems into production-grade models and measurable outcomes that scale funding for ecommerce businesses.

Qualifications

• 5+ years of professional data science or ML experience.
• Strong foundations in statistics, experimentation, and causal reasoning.
• Proven track record delivering production ML models (classification/regression/time series).
• Proficient in Python and SQL, with production data workflows.

Responsibilities

• Design and run data science experiments, including A/B tests and causal analyses.
• Develop, evaluate, and iterate predictive models for credit/risk and revenue forecasting.
• Own model performance, drift monitoring, and data quality improvements.
• Collaborate with Product Eng to productionize models with reliability and reproducibility.
• Perform EDA, feature engineering, and robust validation on real-world data.
• Communicate insights clearly to technical and non-technical stakeholders.
• Improve analytical standards, code reviews, and documentation.
• Mentor teammates and share best practices.

Skills

Python

SQL

Statistics

Experimentation

Machine Learning

Communication

Tools

BigQuery

Snowflake

dbt

Job description

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).