Senior Data Scientist

Clearco

9h • $150,000 - $200,000

About The Position

We are hiring a Senior Data Scientist to build and improve the models, analyses, and experimentation that power Clearco’s risk and revenue decisions. This hands-on senior role is at the intersection of Data Science, Machine Learning, and Product. You will partner closely with Engineering, Product, Risk, and Finance teams to translate ambiguous problems into well-scoped analyses and production-grade solutions. Your work will influence how we assess risk, forecast performance, and responsibly scale funding for eCommerce businesses.

Requirements

• 5+ years of professional experience in data science, applied machine learning, or a related quantitative role
• Strong foundations in statistics and experimentation (hypothesis testing, causal reasoning, bias/variance tradeoffs, evaluation design)
• Proven experience building and shipping predictive models (classification, regression, time series, etc.) and measuring real-world impact
• Proficiency in Python and SQL, with comfort working with production data workflows
• Comfortable working with stakeholders to define problems, align on success metrics, and deliver outcomes end-to-end
• Strong written communication skills and a pragmatic approach to fast-moving environments

Nice To Haves

• Experience with credit risk, underwriting, fraud/risk signals, or financial forecasting
• Familiarity with modern data tooling and warehouses (e.g., BigQuery, Snowflake) and transformation frameworks (e.g., dbt)
• Experience 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, etc.)

Responsibilities

• Design and execute data science experiments such as causal analysis, A/B tests, and offline evaluations to validate product and underwriting decisions.
• Develop, evaluate, and iterate on predictive models (e.g., credit/risk scoring, revenue forecasting, 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, focusing on reliability, reproducibility, and maintainability.
• Turn messy real-world data into usable signals through exploratory analysis, feature engineering, and robust validation.
• Clearly communicate insights to both technical and non-technical stakeholders through documentation and presentations.
• Raise the bar for technical quality via improved analytical standards, code review practices, and documentation.
• Mentor and support other team members through pairing, feedback, and sharing best practices.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed